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Training Machine Learning models at the Edge: A Survey (2403.02619v3)

Published 5 Mar 2024 in cs.LG and cs.DC

Abstract: Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This survey, explores the concept of edge learning, specifically the optimization of ML model training at the edge. The objective is to comprehensively explore diverse approaches and methodologies in edge learning, synthesize existing knowledge, identify challenges, and highlight future trends. Utilizing Scopus and Web of science advanced search, relevant literature on edge learning was identified, revealing a concentration of research efforts in distributed learning methods, particularly federated learning. This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available. In doing so, the paper contributes to a holistic understanding of the current landscape and future directions in the intersection of edge computing and machine learning, paving the way for informed comparisons between optimization methods and techniques designed for training on the edge.

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References (387)
  1. N. Maslej, L. Fattorini, E. Brynjolfsson, J. Etchemendy, K. Ligett, T. Lyons, J. Manyika, H. Ngo, J. C. Niebles, V. Parli et al., “Artificial intelligence index report 2023,” arXiv preprint arXiv:2310.03715, 2023.
  2. K. Cao, Y. Liu, G. Meng, and Q. Sun, “An overview on edge computing research,” IEEE Access, vol. 8, pp. 85 714–85 728, 2020.
  3. S. Ayyasamy, “Edge computing research–a review,” Journal of Information Technology, vol. 5, no. 1, pp. 62–74, 2023.
  4. J. Chen and X. Ran, “Deep learning with edge computing: A review,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1655–1674, 2019.
  5. B. Varghese, N. Wang, S. Barbhuiya, P. Kilpatrick, and D. S. Nikolopoulos, “Challenges and opportunities in edge computing,” in 2016 IEEE International Conference on Smart Cloud (SmartCloud), 2016, pp. 20–26.
  6. B. Lu, J. Yang, and S. Ren, “Poster: Scaling up deep neural network optimization for edge inference,” in 2020 IEEE/ACM Symposium on Edge Computing (SEC), 2020, pp. 170–172.
  7. S. P. Baller, A. Jindal, M. Chadha, and M. Gerndt, “Deepedgebench: Benchmarking deep neural networks on edge devices,” in 2021 IEEE International Conference on Cloud Engineering (IC2E), 2021, pp. 20–30.
  8. C.-J. Wu, D. Brooks, K. Chen, D. Chen, S. Choudhury, M. Dukhan, K. Hazelwood, E. Isaac, Y. Jia, B. Jia et al., “Machine learning at facebook: Understanding inference at the edge,” in 2019 IEEE international symposium on high performance computer architecture (HPCA).   IEEE, 2019, pp. 331–344.
  9. H. G. Abreha, M. Hayajneh, and M. A. Serhani, “Federated Learning in Edge Computing: A Systematic Survey,” Sensors (Basel, Switzerland), vol. 22, no. 2, p. 450, January 2022.
  10. P. Boobalan, S. Ramu, Q.-V. Pham, K. Dev, S. Pandya, P. Maddikunta, T. Gadekallu, and T. Huynh-The, “Fusion of Federated Learning and Industrial Internet of Things: A survey,” Computer Networks, vol. 212, 2022.
  11. S. Zhu, T. Voigt, J. Ko, and F. Rahimian, “On-device Training: A First Overview on Existing Systems,” May 2023, arXiv:2212.00824 [cs].
  12. S. Dhar, J. Guo, J. J. Liu, S. Tripathi, U. Kurup, and M. Shah, “A survey of on-device machine learning: An algorithms and learning theory perspective,” ACM Trans. Internet Things, vol. 2, no. 3, jul 2021.
  13. X. Wang, Y. Han, V. C. M. Leung, D. Niyato, X. Yan, and X. Chen, “Convergence of Edge Computing and Deep Learning: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 869–904, 2020.
  14. Y. Shi, K. Yang, T. Jiang, J. Zhang, and K. B. Letaief, “Communication-Efficient Edge AI: Algorithms and Systems,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2167–2191, 2020.
  15. J. L. Leon Veas, L. B. Cordero Solis, G. E. Valverde Landivar, and M. A. Quiroz Martinez, “Deep learning for edge computing: A survey,” in Artificial Intelligence, Computer and Software Engineering Advances.   Cham: Springer International Publishing, 2021, pp. 79–93.
  16. A. Tak and S. Cherkaoui, “Federated Edge Learning: Design Issues and Challenges,” IEEE Network, vol. 35, no. 2, pp. 252–258, 2021.
  17. M. G. S. Murshed, C. Murphy, D. Hou, N. Khan, G. Ananthanarayanan, and F. Hussain, “Machine Learning at the Network Edge: A Survey,” ACM Computing Surveys, vol. 54, no. 8, pp. 1–37, November 2022.
  18. P. Joshi, M. Hasanuzzaman, C. Thapa, H. Afli, and T. Scully, “Enabling all in-edge deep learning: A literature review,” IEEE Access, vol. 11, pp. 3431–3460, 2023.
  19. H. Cai, J. Lin, Y. Lin, Z. Liu, H. Tang, H. Wang, L. Zhu, and S. Han, “Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications,” ACM Transactions on Design Automation of Electronic Systems, vol. 27, no. 3, 2022.
  20. Y. Cui, J. Guo, X. Li, L. Liang, and S. Jin, “Federated edge learning for the wireless physical layer: Opportunities and challenges,” China Communications, vol. 19, no. 8, pp. 15–30, August 2022, conference Name: China Communications.
  21. A. Imteaj, U. Thakker, S. Wang, J. Li, and M. Amini, “A Survey on Federated Learning for Resource-Constrained IoT Devices,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1–24, 2022.
  22. R. Singh and S. S. Gill, “Edge AI: A survey,” Internet of Things and Cyber-Physical Systems, vol. 3, pp. 71–92, January 2023.
  23. W. Li, H. Hacid, E. Almazrouei, and M. Debbah, “A comprehensive review and a taxonomy of edge machine learning: Requirements, paradigms, and techniques,” AI, vol. 4, no. 3, pp. 729–786, 2023.
  24. H. Hua, Y. Li, T. Wang, N. Dong, W. Li, and J. Cao, “Edge Computing with Artificial Intelligence: A Machine Learning Perspective,” ACM Computing Surveys, vol. 55, no. 9, pp. 184:1–184:35, January 2023.
  25. Z. Wu, S. Sun, Y. Wang, M. Liu, X. Jiang, R. Li, and B. Gao, “Survey of Knowledge Distillation in Federated Edge Learning,” February 2023, arXiv:2301.05849 [cs].
  26. C. P. Bailey, A. C. Depoian, and E. R. Adams, “Edge AI: Addressing the Efficiency Paradigm,” in 2022 IEEE MetroCon, November 2022, pp. 1–3.
  27. M. Satyanarayanan, “Mobile computing,” Computer, vol. 26, no. 9, pp. 81–82, September 1993, conference Name: Computer.
  28. M. Satyanarayanan, N. Beckmann, G. A. Lewis, and B. Lucia, “The Role of Edge Offload for Hardware-Accelerated Mobile Devices,” in Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications, ser. HotMobile ’21.   New York, NY, USA: Association for Computing Machinery, February 2021, pp. 22–29.
  29. H. Cai, C. Gan, L. Zhu, and S. Han, “TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning,” in Advances in Neural Information Processing Systems, vol. 33.   Curran Associates, Inc., 2020, pp. 11 285–11 297.
  30. J. Lin, L. Zhu, W.-M. Chen, W.-C. Wang, C. Gan, and S. Han, “On-device training under 256kb memory,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35.   Curran Associates, Inc., 2022, pp. 22 941–22 954.
  31. A. Tashakori, W. Zhang, Z. Jane Wang, and P. Servati, “Semipfl: Personalized semi-supervised federated learning framework for edge intelligence,” IEEE Internet of Things Journal, vol. 10, no. 10, pp. 9161–9176, 2023.
  32. K. Afachao and A. M. Abu-Mahfouz, “A review of intelligent iot devices at the edge,” 2022 International Conference on Artificial Intelligence of Things (ICAIoT), pp. 1–6, 2022.
  33. F. Bellotti, R. Berta, A. De Gloria, J. Doyle, and F. Sakr, “Exploring Unsupervised Learning on STM32 F4 Microcontroller,” in Applications in Electronics Pervading Industry, Environment and Society, vol. 738, 2021, pp. 39–46.
  34. W. Zhu and Z. Lu, “Evaluation of time series clustering on embedded sensor platform,” in 2021 24th Euromicro Conference on Digital System Design (DSD), 2021, pp. 187–191.
  35. M. T. Yazici, S. Basurra, and M. M. Gaber, “Edge machine learning: Enabling smart internet of things applications,” Big Data and Cognitive Computing, vol. 2, no. 3, 2018.
  36. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research, A. Singh and J. Zhu, Eds., vol. 54.   PMLR, 20–22 Apr 2017, pp. 1273–1282.
  37. S. Abbas, A. Hejaili, G. Sampedro, M. Abisado, A. Almadhor, T. Shahzad, and K. Ouahada, “A Novel Federated Edge Learning Approach for Detecting Cyberattacks in IoT Infrastructures,” IEEE Access, vol. 11, pp. 112 189–112 198, 2023.
  38. Z. Li, X. Cheng, J. Zhang, and B. Chen, “Predicting Advanced Persistent Threats for IoT Systems Based on Federated Learning,” in Security, Privacy, and Anonymity in Computation, Communication, and Storage, vol. 12382 LNCS, 2021, pp. 76–89.
  39. J. Sidhpura, P. Shah, R. Veerkhare, and A. Godbole, “FedSpam: Privacy Preserving SMS Spam Prediction,” in Neural Information Processing, vol. 1793 CCIS.   Springer Nature Singapore, 2023, pp. 52–63.
  40. S. Sriraman, S. Kannan, S. Ravishankar, and B. Bharathi, “An On-device Federated Learning System for SMS Spam Classification,” in 2022 IEEE MIT Undergraduate Research Technology Conference (URTC), 2022.
  41. M. El Hanjri, H. Kabbaj, A. Kobbane, and A. Abouaomar, “Federated Learning for Water Consumption Forecasting in Smart Cities,” in ICC 2023 - IEEE International Conference on Communications, vol. 2023-May, 2023, pp. 1798–1803.
  42. Y. Shi, X. Li, and S. Chen, “Towards Smart and Efficient Service Systems: Computational Layered Federated Learning Framework,” IEEE Network, pp. 1–8, 2023.
  43. Y. Cheriguene, W. Jaafar, H. Yanikomeroglu, and C. Kerrache, “Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data,” IEEE Open Journal of Vehicular Technology, vol. 5, pp. 125–141, 2024.
  44. A. Das and T. Brunschwiler, “Privacy is what we care about: Experimental investigation of federated learning on edge devices,” in Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things.   Association for Computing Machinery, 2019, pp. 39–42.
  45. B. Jiang, J. Li, H. Wang, and H. Song, “Privacy-Preserving Federated Learning for Industrial Edge Computing via Hybrid Differential Privacy and Adaptive Compression,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1136–1144, 2023.
  46. T. Liu, B. Di, and L. Song, “Privacy-Preserving Federated Edge Learning: Modeling and Optimization,” IEEE Communications Letters, vol. 26, no. 7, pp. 1489–1493, 2022.
  47. Y. Ye, S. Li, F. Liu, Y. Tang, and W. Hu, “EdgeFed: Optimized Federated Learning Based on Edge Computing,” IEEE Access, vol. 8, pp. 209 191–209 198, 2020.
  48. R. Liu, Y. Cao, M. Yoshikawa, and H. Chen, “FedSel: Federated SGD Under Local Differential Privacy with Top-k Dimension Selection,” in Database Systems for Advanced Applications, vol. 12112 LNCS.   Springer International Publishing, 2020, pp. 485–501.
  49. S. Mahara, M. Shruti, and B. Bharath, “Multi-Task Federated Edge Learning (MTFeeL) With SignSGD,” in 2022 National Conference on Communications (NCC), 2022, pp. 379–384.
  50. Y. Zeng, Y. Mu, J. Yuan, S. Teng, J. Zhang, J. Wan, Y. Ren, and Y. Zhang, “Adaptive Federated Learning With Non-IID Data,” Computer Journal, vol. 66, no. 11, pp. 2758–2772, 2023.
  51. B. Alhalabi, S. Basurra, and M. Gaber, “FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks,” IEEE Access, vol. 11, pp. 30 726–30 738, 2023.
  52. B. Zhao, T. Wang, and L. Fang, “FedCom: Byzantine-Robust Federated Learning Using Data Commitment,” in ICC 2023 - IEEE International Conference on Communications, vol. 2023-May, 2023, pp. 33–38.
  53. Y. Kim and C.-J. Wu, “FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning,” in 2022 IEEE International Symposium on Workload Characterization (IISWC), 2022, pp. 117–129.
  54. Y. Liu, Y. Zhu, and J. Yu, “Resource-Constrained Federated Edge Learning With Heterogeneous Data: Formulation and Analysis,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3166–3178, 2022.
  55. H. Lv, Z. Zheng, T. Luo, F. Wu, S. Tang, L. Hua, R. Jia, and C. Lv, “Data-Free Evaluation of User Contributions in Federated Learning,” in 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2021.
  56. G. Gudur and S. Perepu, “Zero-shot federated learning with new classes for audio classification,” in Proc. Interspeech 2021, vol. 2, 2021, pp. 1041–1045.
  57. S. Savazzi, M. Nicoli, and V. Rampa, “Federated learning with cooperating devices: A consensus approach for massive iot networks,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4641–4654, 2020.
  58. M. Gupta, P. Goyal, R. Verma, R. Shorey, and H. Saran, “Fedfm: Towards a robust federated learning approach for fault mitigation at the edge nodes,” in 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2022, pp. 362–370.
  59. A. Agrawal, D. Kulkarni, and S. Nair, “On Decentralizing Federated Learning,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), vol. 2020-October, 2020, pp. 1590–1595.
  60. H. Gu, B. Guo, J. Wang, W. Sun, J. Liu, S. Liu, and Z. Yu, “FedAux: An Efficient Framework for Hybrid Federated Learning,” in ICC 2022 - IEEE International Conference on Communications, vol. 2022-May, 2022, pp. 195–200.
  61. F. Zhang, J. Ge, C. Wong, S. Zhang, C. Li, and B. Luo, “Optimizing Federated Edge Learning on Non-IID Data via Neural Architecture Search,” in 2021 IEEE Global Communications Conference (GLOBECOM), 2021.
  62. R. Sharma, A. Ramakrishna, A. MacLaughlin, A. Rumshisky, J. Majmudar, C. Chung, S. Avestimehr, and R. Gupta, “Federated Learning with Noisy User Feedback,” arXiv preprint arXiv:2205.03092, pp. 2726–2739, 2022.
  63. K. Yang, T. Jiang, Y. Shi, and Z. DIng, “Federated Learning Based on Over-the-Air Computation,” in ICC 2019 - 2019 IEEE International Conference on Communications (ICC), vol. 2019-May, 2019.
  64. B. Xiao, X. Yu, W. Ni, X. Wang, and H. V. Poor, “Over-the-air federated learning: Status quo, open challenges, and future directions,” arXiv preprint arXiv:2307.00974, 2023.
  65. N. Mital and D. Gunduz, “Bandwidth Expansion for Over-the-Air Computation with One-Sided CSI,” in 2022 IEEE International Symposium on Information Theory (ISIT), vol. 2022-June, 2022, pp. 1271–1276.
  66. Y. Shao, D. Gunduz, and S. Liew, “Federated Edge Learning With Misaligned Over-the-Air Computation,” IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 3951–3964, 2022.
  67. O. Aygün, M. Kazemi, D. Gündüz, and T. Duman, “Hierarchical Over-the-Air Federated Edge Learning,” in ICC 2022 - IEEE International Conference on Communications, vol. 2022-May, 2022, pp. 3376–3381.
  68. J. Jiang, K. Han, Y. Du, G. Zhu, Z. Wang, and S. Cui, “Optimized Power Control for Over-the-Air Federated Averaging With Data Privacy Guarantee,” IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 2728–2733, 2023.
  69. A. Sahin, “Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning,” IEEE Transactions on Wireless Communications, pp. 1–1, 2023.
  70. A. Bemani and N. Björsell, “Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments,” Sensors, vol. 23, no. 18, 2023.
  71. X. Cao, G. Zhu, J. Xu, Z. Wang, and S. Cui, “Optimized Power Control Design for Over-the-Air Federated Edge Learning,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 1, pp. 342–358, 2022.
  72. L. Liu, J. Zhang, S. Song, and K. Letaief, “Client-Edge-Cloud Hierarchical Federated Learning,” in ICC 2020 - 2020 IEEE International Conference on Communications (ICC), vol. 2020-June, 2020.
  73. M. Abad, E. Ozfatura, D. Gündüz, and O. Ercetin, “Hierarchical federated learning across heterogeneous cellular networks,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 2020-May, 2020, pp. 8866–8870.
  74. M. Ma, L. Wu, W. Liu, N. Chen, Z. Shao, and Y. Yang, “Data-aware Hierarchical Federated Learning via Task Offloading,” in 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings, 2022, pp. 3011–3016.
  75. W. Wen, H. Yang, W. Xia, and T. Quek, “Towards Fast and Energy-Efficient Hierarchical Federated Edge Learning: A Joint Design for Helper Scheduling and Resource Allocation,” in IEEE International Conference on Communications, vol. 2022-May, 2022, pp. 5378–5383.
  76. M. Amiri, T. Duman, D. Gunduz, S. Kulkarni, and H. Poor, “Blind Federated Edge Learning,” IEEE Transactions on Wireless Communications, vol. 20, no. 8, pp. 5129–5143, 2021.
  77. K.-Y. Liang, A. Srinivasan, and J. Andresen, “Modular Federated Learning,” in Proceedings of the International Joint Conference on Neural Networks, vol. 2022-July, 2022.
  78. F. Sattler, K.-R. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 8, pp. 3710–3722, 2021.
  79. T. Fan, Y. Kang, G. Ma, W. Chen, W. Wei, L. Fan, and Q. Yang, “Fate-llm: A industrial grade federated learning framework for large language models,” arXiv preprint arXiv:2310.10049, 2023.
  80. M. Xu, D. Cai, Y. Wu, X. Li, and S. Wang, “Fwdllm: Efficient fedllm using forward gradient,” 2024.
  81. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
  82. Y. Tian, Y. Wan, L. Lyu, D. Yao, H. Jin, and L. Sun, “FedBERT: When Federated Learning Meets Pre-training,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 4, pp. 66:1–66:26, August 2022.
  83. Z. Lit, S. Sit, J. Wang, and J. Xiao, “Federated split bert for heterogeneous text classification,” in 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1–8.
  84. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019.
  85. J. Tao, Z. Gao, and Z. Guo, “Training Vision Transformers in Federated Learning with Limited Edge-Device Resources,” Electronics, vol. 11, no. 17, p. 2638, January 2022, number: 17 Publisher: Multidisciplinary Digital Publishing Institute.
  86. T.-M. Hsu, H. Qi, and M. Brown, “Federated Visual Classification with Real-World Data Distribution,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12355 LNCS, 2020, pp. 76–92.
  87. W. Yu, J. Freiwald, S. Tewes, F. Huennemeyer, and D. Kolossa, “Federated Learning in ASR: Not as Easy as You Think,” in 14th ITG Conference on Speech Communication, 2021, pp. 19–23.
  88. J. Jia, J. Mahadeokar, W. Zheng, Y. Shangguan, O. Kalinli, and F. Seide, “Federated Domain Adaptation for ASR with Full Self-Supervision,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2022-September, 2022, pp. 536–540.
  89. D. Guliani, L. Zhou, C. Ryu, T.-J. Yang, H. Zhang, Y. Xiao, F. Beaufays, and G. Motta, “Enabling On-Device training of speech recognition models with federated dropout,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2022-May, 2022, pp. 8757–8761.
  90. D. Guliani, F. Beaufays, and G. Motta, “Training speech recognition models with federated learning: A quality/cost framework,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2021-June, 2021, pp. 3080–3084.
  91. C. Bai, X. Cui, and A. Li, “Robust speech recognition model using multi-source federal learning after distillation and deep edge intelligence,” in Journal of Physics: Conference Series, vol. 2033, 2021, issue: 1.
  92. T. Zhang, T. Feng, S. Alam, S. Lee, M. Zhang, S. S. Narayanan, and S. Avestimehr, “Fedaudio: A federated learning benchmark for audio tasks,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2023, pp. 1–5.
  93. C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, “SplitFed: When federated learning meets split learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 8, 2022, pp. 8485–8493.
  94. D. Zou, X. Liu, L. Sun, J. Duan, R. Li, Y. Xu, W. Li, and S. Lu, “FedMC: Federated Reinforcement Learning on the Edge with Meta-Critic Networks,” in Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference, vol. 2022-November, 2022, pp. 344–351.
  95. S. Yue, J. Ren, J. Xin, D. Zhang, Y. Zhang, and W. Zhuang, “Efficient Federated Meta-Learning over Multi-Access Wireless Networks,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 5, pp. 1556–1570, 2022.
  96. J. Suzuki, S. Lameh, and Y. Amannejad, “Using Transfer Learning in Building Federated Learning Models on Edge Devices,” in 2021 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021, 2021, pp. 105–113.
  97. E. Tanghatari, M. Kamal, A. Afzali-Kusha, and M. Pedram, “Federated learning by employing knowledge distillation on edge devices with limited hardware resources,” Neurocomputing, vol. 531, pp. 87–99, April 2023.
  98. X. Qu, J. Wang, and J. Xiao, “Quantization and Knowledge Distillation for Efficient Federated Learning on Edge Devices,” in Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020, 2020, pp. 967–972.
  99. L. Liu, J. Zhang, S. Song, and K. Letaief, “Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design,” IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 2–18, 2023.
  100. K. Palanisamy, V. Khimani, M. H. Moti, and D. Chatzopoulos, “SplitEasy: A Practical Approach for Training ML models on Mobile Devices,” in Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications, February 2021, pp. 37–43.
  101. S. Liu, L. Xin, X. Lyu, and C. Ren, “Masking-enabled Data Protection Approach for Accurate Split Learning,” in IEEE Wireless Communications and Networking Conference, WCNC, vol. 2023-March, 2023, iSSN: 1525-3511.
  102. S. Fu, F. Dong, D. Shen, and Q. He, “Joint Quality Evaluation, Model Splitting and Resource Provisioning for Split Edge Learning,” in Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, vol. 2023-September, 2023, pp. 420–428, iSSN: 2155-5486.
  103. A. Chopra, S. K. Sahu, A. Singh, A. Java, P. Vepakomma, M. M. Amiri, and R. Raskar, “Adaptive Split Learning,” in Federated Learning Systems (FLSys) Workshop @ MLSys 2023, July 2023.
  104. A. Chopra, S. K. Sahu, A. Singh, A. Java, P. Vepakomma, V. Sharma, and R. Raskar, “AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning,” December 2021, arXiv:2112.01637 [cs].
  105. E. Samikwa, A. D. Maio, and T. Braun, “ARES: Adaptive Resource-Aware Split Learning for Internet of Things,” Computer Networks, vol. 218, p. 109380, December 2022.
  106. A. Ayad, M. Renner, and A. Schmeink, “Improving the Communication and Computation Efficiency of Split Learning for IoT Applications,” in 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings, 2021.
  107. Z. Cheng, X. Xia, M. Liwang, X. Fan, Y. Sun, X. Wang, and L. Huang, “CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 12, pp. 3174–3191, 2023.
  108. Q. Duan, S. Hu, R. Deng, and Z. Lu, “Combined federated and split learning in edge computing for ubiquitous intelligence in internet of things: State-of-the-art and future directions,” Sensors, vol. 22, no. 16, 2022.
  109. C. Li, H. Yang, Z. Sun, Q. Yao, J. Zhang, A. Yu, A. Vasilakos, S. Liu, and Y. Li, “High-Precision Cluster Federated Learning for Smart Home: An Edge-Cloud Collaboration Approach,” IEEE Access, vol. 11, pp. 102 157–102 168, 2023.
  110. S. Fu, F. Dong, D. Shen, and T. Lu, “Privacy-preserving model splitting and quality-aware device association for federated edge learning,” Software - Practice and Experience, 2023.
  111. S. Zhang, H. Tu, Z. Li, S. Liu, S. Li, W. Wu, and X. Shen, “Cluster-HSFL: A Cluster-Based Hybrid Split and Federated Learning,” in 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023, 2023.
  112. Y. Wang, Z. Tian, X. Fan, Y. Huo, C. Nowzari, and K. Zeng, “Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence,” October 2022.
  113. X. Fan, Y. Wang, Y. Huo, and Z. Tian, “Efficient Distributed Swarm Learning for Edge Computing,” in IEEE International Conference on Communications, vol. 2023-May, 2023, pp. 3627–3632, iSSN: 1550-3607.
  114. I. Hegedűs, G. Danner, and M. Jelasity, “Gossip Learning as a Decentralized Alternative to Federated Learning,” in Distributed Applications and Interoperable Systems, ser. Lecture Notes in Computer Science, J. Pereira and L. Ricci, Eds.   Cham: Springer International Publishing, 2019, pp. 74–90.
  115. S.-M. Bagoly and R. Danescu, “Round Based Extension Algorithm for Gossip Learning,” in Proceedings - 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing, ICCP 2020, 2020, pp. 251–257.
  116. S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345–1359, 2009.
  117. L. Yang, A. Rakin, and D. Fan, “RepNet: Efficient On-Device Learning via Feature Reprogramming,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, 2022, pp. 12 267–12 276.
  118. H.-Y. Chiang, N. Frumkin, F. Liang, and D. Marculescu, “MobileTL: On-Device Transfer Learning with Inverted Residual Blocks,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, 2023, pp. 7166–7174.
  119. B. Yang, O. Fagbohungbe, X. Cao, C. Yuen, L. Qian, D. Niyato, and Y. Zhang, “A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks,” IEEE Transactions on Industrial Informatics, vol. 18, no. 1, pp. 531–541, 2022.
  120. S. Choi, J. Shin, and L.-S. Kim, “Accelerating on-device dnn training workloads via runtime convergence monitor,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 5, pp. 1574–1587, 2022.
  121. K. Ahmed, A. Imteaj, and M. Amini, “Federated Deep Learning for Heterogeneous Edge Computing,” in Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, 2021, pp. 1146–1152.
  122. S. Liu, S. Xu, W. Yu, Z. Fu, Y. Zhang, and A. Marian, “FedCT: Federated Collaborative Transfer for Recommendation,” in SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 716–725.
  123. D. Vucetic, M. Tayaranian, M. Ziaeefard, J. Clark, B. Meyer, and W. Gross, “Efficient Fine-Tuning of BERT Models on the Edge,” in Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2022-May, 2022, pp. 1838–1842.
  124. Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu, “Large Scale Incremental Learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 374–382.
  125. Q. Yang, Y. Gu, and D. Wu, “Survey of incremental learning,” in 2019 chinese control and decision conference (ccdc).   IEEE, 2019, pp. 399–404.
  126. J. Zuo, G. Arvanitakis, and H. Hacid, “On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge,” February 2023, arXiv:2302.09310 [cs].
  127. G. SHI, J. CHEN, W. Zhang, L.-M. Zhan, and X.-M. Wu, “Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima,” in Advances in Neural Information Processing Systems, vol. 34.   Curran Associates, Inc., 2021, pp. 6747–6761.
  128. H.-G. Doan, H.-Q. Luong, T.-O. Ha, and T. T. T. Pham, “An Efficient Strategy for Catastrophic Forgetting Reduction in Incremental Learning,” Electronics, vol. 12, no. 10, p. 2265, January 2023, number: 10 Publisher: Multidisciplinary Digital Publishing Institute.
  129. M. A. Hussain, S.-A. Huang, and T.-H. Tsai, “Learning With Sharing: An Edge-Optimized Incremental Learning Method for Deep Neural Networks,” IEEE Transactions on Emerging Topics in Computing, vol. 11, no. 2, pp. 461–473, April 2023, conference Name: IEEE Transactions on Emerging Topics in Computing.
  130. S. Disabato and M. Roveri, “Incremental On-Device Tiny Machine Learning,” in AIChallengeIoT 2020 - Proceedings of the 2020 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, 2020, pp. 7–13.
  131. J. Liu, Z. Xie, D. Nikolopoulos, and D. Li, “RIANN: Real-time incremental learning with approximate nearest neighbor on mobile devices,” in OpML 2020 - 2020 USENIX Conference on Operational Machine Learning, 2020.
  132. D. Li, S. Tasci, S. Ghosh, J. Zhu, J. Zhang, and L. Heck, “RILOD: near real-time incremental learning for object detection at the edge,” in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, ser. SEC ’19.   New York, NY, USA: Association for Computing Machinery, November 2019, pp. 113–126.
  133. M. Rao, G. Chennupati, G. Tiwari, A. Kumar Sahu, A. Raju, A. Rastrow, and J. Droppo, “Federated Self-Learning with Weak Supervision for Speech Recognition,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2023.
  134. S. Yue, J. Ren, J. Xin, S. Lin, and J. Zhang, “Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning,” in Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, July 2021, pp. 91–100, arXiv:2012.08677 [cs].
  135. Y. Luo, Z. Huang, Z. Zhang, Z. Wang, M. Baktashmotlagh, and Y. Yang, “Learning from the past: Continual meta-learning with bayesian graph neural networks,” in AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020, pp. 5021–5028.
  136. Z.-H. Wang, Z. He, H. Fang, Y.-X. Huang, Y. Sun, Y. Yang, Z.-Y. Zhang, and D. Liu, “Efficient On-Device Incremental Learning by Weight Freezing,” in 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), January 2022, pp. 538–543, iSSN: 2153-697X.
  137. A. Carta, A. Cossu, V. Lomonaco, D. Bacciu, and J. van de Weijer, “Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning,” March 2023, arXiv:2303.15888 [cs] version: 1.
  138. X. Zhang, H. Li, X. Chen, and X. Liu, “Impact Patterns of Combining Model Pruning and Continual Learning on Model Performance,” in Proceedings - 2021 IEEE 3rd International Conference on Cognitive Machine Intelligence, CogMI 2021, 2021, pp. 27–33.
  139. Z. Wang, Z. Zhan, Y. Gong, G. Yuan, W. Niu, T. Jian, B. Ren, S. Ioannidis, Y. Wang, and J. Dy, “SparCL: Sparse Continual Learning on the Edge,” in Advances in Neural Information Processing Systems, vol. 35, 2022.
  140. A. Nichol, J. Achiam, and J. Schulman, “On First-Order Meta-Learning Algorithms,” October 2018, arXiv:1803.02999 [cs].
  141. Z. Qu, Z. Zhou, Y. Tong, and L. Thiele, “p-Meta: Towards On-device Deep Model Adaptation,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 2022, pp. 1441–1451, arXiv:2206.12705 [cs].
  142. B. Rosenfeld, B. Rajendran, and O. Simeone, “Fast on-device adaptation for spiking neural networks via online-within-online meta-learning,” in 2021 IEEE Data Science and Learning Workshop, DSLW 2021, 2021.
  143. D. Gao, X. He, Z. Zhou, Y. Tong, and L. Thiele, “Pruning Meta-Trained Networks for On-Device Adaptation,” in International Conference on Information and Knowledge Management, Proceedings, 2021, pp. 514–523.
  144. F. Yu, H. Lin, X. Wang, S. Garg, G. Kaddoum, S. Singh, and M. M. Hassan, “Communication-Efficient Personalized Federated Meta-Learning in Edge Networks,” IEEE Transactions on Network and Service Management, vol. 20, no. 2, pp. 1558–1571, June 2023, conference Name: IEEE Transactions on Network and Service Management.
  145. J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,” International Journal of Computer Vision, vol. 129, pp. 1789–1819, 2021.
  146. H. Nam, J. Park, and S.-L. Kim, “Active Wireless Split Learning via Online Cloud-Local Server Delta-Knowledge Distillation,” in 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023, 2023, pp. 825–830.
  147. X. Xia, H. Yin, J. Yu, Q. Wang, G. Xu, and Q. Nguyen, “On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation,” in SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 546–555.
  148. Y.-G. Qian, J. Ma, N.-N. He, B. Wang, Z.-Q. Gu, X. Ling, and S. Wassim, “Two-stage Adversarial Knowledge Transfer for Edge Intelligence,” Ruan Jian Xue Bao/Journal of Software, vol. 33, no. 12, pp. 4504–4516, 2022.
  149. Y. Zhou, X. Ma, D. Wu, and X. Li, “Communication-Efficient and Attack-Resistant Federated Edge Learning with Dataset Distillation,” IEEE Transactions on Cloud Computing, vol. 11, no. 3, pp. 2517–2528, 2023.
  150. A. Hard, K. Partridge, N. Chen, S. Augenstein, A. Shah, H. Park, A. Park, S. Ng, J. Nguyen, I. Moreno, R. Mathews, and F. Beaufays, “Production federated keyword spotting via distillation, filtering, and joint federated-centralized training,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2022-September, 2022, pp. 76–80.
  151. S. Oh, J. Park, E. Jeong, H. Kim, M. Bennis, and S.-L. Kim, “Mix2FLD: Downlink Federated Learning after Uplink Federated Distillation with Two-Way Mixup,” IEEE Communications Letters, vol. 24, no. 10, pp. 2211–2215, 2020.
  152. J.-H. Ahn, O. Simeone, and J. Kang, “Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data,” in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, vol. 2019-September, 2019.
  153. D. Nguyen, S. Yu, J. Muñoz, and A. Jannesari, “Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion,” in ACM International Conference Proceeding Series, 2023, pp. 36–43.
  154. I. Bistritz, A. Mann, and N. Bambos, “Distributed Distillation for On-Device Learning,” in Advances in Neural Information Processing Systems, vol. 33.   Curran Associates, Inc., 2020, pp. 22 593–22 604.
  155. X. Xia, J. Yu, Q. Wang, C. Yang, N. Hung, and H. Yin, “Efficient On-Device Session-Based Recommendation,” ACM Transactions on Information Systems, vol. 41, no. 4, 2023.
  156. J. Yao, F. Wang, K. Jia, B. Han, J. Zhou, and H. Yang, “Device-Cloud Collaborative Learning for Recommendation,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021, pp. 3865–3874.
  157. J. Wong, J. Nerbonne, and Q. Zhang, “Ultra-efficient edge cardiac disease detection towards real-time precision health,” IEEE Access, pp. 1–1, 2023.
  158. I. Jang, H. Kim, D. Lee, Y.-S. Son, and S. Kim, “Knowledge Transfer for On-Device Deep Reinforcement Learning in Resource Constrained Edge Computing Systems,” IEEE Access, vol. 8, pp. 146 588–146 597, 2020.
  159. M. R. Sebti, A. Accettola, R. Carotenuto, and M. Merenda, “Dataset Distillation Technique Enabling ML On-board Training: Preliminary Results,” in Proceedings of SIE 2023, ser. Lecture Notes in Electrical Engineering, C. Ciofi and E. Limiti, Eds.   Cham: Springer Nature Switzerland, 2024, pp. 379–384.
  160. A. G. Accettola and M. Merenda, “Dataset distillation as an enabling technique for on-device training in TinyML for IoT: an RFID use case,” in 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech), June 2023, pp. 1–4.
  161. H. Hu, S. Siniscalchi, C.-H. Yang, and C.-H. Lee, “A VARIATIONAL Bayesian APPROACH TO LEARNING LATENT VARIABLES FOR ACOUSTIC KNOWLEDGE TRANSFER,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2022-May, 2022, pp. 1041–1045, iSSN: 1520-6149.
  162. A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer, “A survey of quantization methods for efficient neural network inference,” in Low-Power Computer Vision.   Chapman and Hall/CRC, 2022, pp. 291–326.
  163. A. Kwasniewska, M. Szankin, M. Ozga, J. Wolfe, A. Das, A. Zajac, J. Ruminski, and P. Rad, “Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters,” in IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, October 2019, pp. 96–101, iSSN: 2577-1647.
  164. M. Ostertag, S. Al-Doweesh, and T. Rosing, “Efficient Training on Edge Devices Using Online Quantization,” in Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020, 2020, pp. 1011–1014.
  165. Y. Li, Y. Cui, and V. Lau, “An Optimization Framework for Federated Edge Learning,” IEEE Transactions on Wireless Communications, vol. 22, pp. 934–949, February 2023.
  166. S. Choi, J. Shin, Y. Choi, and L.-S. Kim, “An optimized design technique of low-bit neural network training for personalization on IoT devices,” in Proceedings - Design Automation Conference, 2019, iSSN: 0738-100X.
  167. Y. Chen, C. Hawkins, K. Zhang, Z. Zhang, and C. Hao, “3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low Bitwidth Quantization, and Ultra-Low Latency Acceleration,” in Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, 2021, pp. 157–162.
  168. H. Li, R. Wang, W. Zhang, and J. Wu, “One Bit Aggregation for Federated Edge Learning with Reconfigurable Intelligent Surface: Analysis and Optimization,” IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 872–888, 2023.
  169. G. Zhu, Y. Du, D. Gündüz, and K. Huang, “One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis,” IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 2120–2135, 2021.
  170. H. Li, R. Wang, J. Wu, and W. Zhang, “Federated edge learning via reconfigurable intelligent surface with one-bit quantization,” in GLOBECOM 2022-2022 IEEE Global Communications Conference.   IEEE, 2022, pp. 1055–1060.
  171. Y. Cui, J. Guo, C. Wen, and S. Jin, “Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback,” IEEE Transactions on Wireless Communications, pp. 1–1, 2023.
  172. H. Yan, B. Tang, and B. Ye, “Joint Optimization of Bandwidth Allocation and Gradient Quantization for Federated Edge Learning,” in Lecture Notes in Computer Science, January 2022, pp. 444–455, iSSN: 0302-9743.
  173. Z. Ren, W. Fang, W. Xu, Z. Li, and Y. Hu, “Research on Lightweight Model Training Technology of Federated Learning for Railway Defect Detection,” Tiedao Xuebao/Journal of the China Railway Society, vol. 45, no. 4, pp. 77–83, 2023.
  174. R. Chen, L. Li, K. Xue, C. Zhang, M. Pan, and Y. Fang, “Energy Efficient Federated Learning Over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission,” IEEE Transactions on Mobile Computing, vol. 22, no. 12, pp. 7451–7465, 2023.
  175. P. Liu, J. Jiang, G. Zhu, L. Cheng, W. Jiang, W. Luo, Y. Du, and Z. Wang, “Training time minimization for federated edge learning with optimized gradient quantization and bandwidth allocation,” Frontiers of Information Technology & Electronic Engineering, vol. 23, no. 8, pp. 1247–1263, August 2022.
  176. J. Chauhan, Y. D. Kwon, and C. Mascolo, “Exploring On-Device Learning Using Few Shots for Audio Classification,” in 2022 30th European Signal Processing Conference (EUSIPCO), August 2022, pp. 424–428, iSSN: 2076-1465.
  177. Y. Yamagishi, T. Kaneko, M. Akai-Kasaya, and T. Asai, “Holmes: A Hardware-Oriented Optimizer Using Logarithms,” IEICE Transactions on Information and Systems, vol. E105D, no. 12, pp. 2040–2047, 2022.
  178. X. Zhou and D. Yan, “Model tree pruning,” International Journal of Machine Learning and Cybernetics, vol. 10, pp. 3431–3444, 2019.
  179. W. Kwon, S. Kim, M. W. Mahoney, J. Hassoun, K. Keutzer, and A. Gholami, “A fast post-training pruning framework for transformers,” Advances in Neural Information Processing Systems, vol. 35, pp. 24 101–24 116, 2022.
  180. S. Choi, J. Shin, and L.-S. Kim, “A convergence monitoring method for dnn training of on-device task adaptation,” in 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD).   IEEE, 2021, pp. 1–9.
  181. S. Yu, P. Nguyen, A. Anwar, and A. Jannesari, “Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient,” in Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023, 2023, pp. 322–330.
  182. Y. Jiang, S. Wang, V. Valls, B. Ko, W.-H. Lee, K. Leung, and L. Tassiulas, “Model Pruning Enables Efficient Federated Learning on Edge Devices,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10 374–10 386, 2023.
  183. S. Liu, G. Yu, R. Yin, J. Yuan, L. Shen, and C. Liu, “Joint Model Pruning and Device Selection for Communication-Efficient Federated Edge Learning,” IEEE Transactions on Communications, vol. 70, no. 1, pp. 231–244, 2022.
  184. N. Mairittha, T. Mairittha, and S. Inoue, “On-device deep personalization for robust activity data collection†,” Sensors (Switzerland), vol. 21, no. 1, pp. 1–22, 2021.
  185. J. Han, Y. Ma, Q. Mei, and X. Liu, “Deeprec: On-device deep learning for privacy-preserving sequential recommendation in mobile commerce,” in The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, 2021, pp. 900–911.
  186. J. Lee, S. Kim, S. Kim, W. Jo, J.-H. Kim, D. Han, and H.-J. Yoo, “OmniDRL: An Energy-Efficient Deep Reinforcement Learning Processor with Dual-Mode Weight Compression and Sparse Weight Transposer,” IEEE Journal of Solid-State Circuits, vol. 57, no. 4, pp. 999–1012, 2022.
  187. A. Hosny, M. Neseem, and S. Reda, “Sparse Bitmap Compression for Memory-Efficient Training on the Edge,” in 6th ACM/IEEE Symposium on Edge Computing, SEC 2021, 2021, pp. 14–25.
  188. M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,” arXiv preprint arXiv:1602.02830, 2016.
  189. E. Wang, J. J. Davis, D. Moro, P. Zielinski, J. J. Lim, C. Coelho, S. Chatterjee, P. Y. K. Cheung, and G. A. Constantinides, “Enabling Binary Neural Network Training on the Edge,” ACM Transactions on Embedded Computing Systems, vol. 22, no. 6, pp. 105:1–105:19, November 2023.
  190. L. Vorabbi, D. Maltoni, and S. Santi, “On-Device Learning with Binary Neural Networks,” pp. 39–50, 2024.
  191. Y. Fujiwara and T. Kawahara, “BNN Training Algorithm with Ternary Gradients and BNN based on MRAM Array,” in TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), October 2023, pp. 311–316, iSSN: 2159-3450.
  192. B. Penkovsky, M. Bocquet, T. Hirtzlin, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal, and D. Querlioz, “In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications,” in Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020, 2020, pp. 690–695.
  193. N. D. Pham, H. D. Nguyen, and D. H. Dang, “Efficient binarizing split learning based deep models for mobile applications,” AIP Conference Proceedings, vol. 2406, no. 1, p. 020015, September 2021.
  194. T. Tang, R. Luo, B. Li, H. Li, Y. Wang, and H. Yang, “Energy efficient spiking neural network design with rram devices,” in 2014 International Symposium on Integrated Circuits (ISIC).   IEEE, 2014, pp. 268–271.
  195. E. Lemaire, L. Cordone, A. Castagnetti, P.-E. Novac, J. Courtois, and B. Miramond, “An analytical estimation of spiking neural networks energy efficiency,” in International Conference on Neural Information Processing.   Springer, 2022, pp. 574–587.
  196. J. Xue, L. Xie, F. Chen, L. Wu, Q. Tian, Y. Zhou, R. Ying, and P. Liu, “Edgemap: An optimized mapping toolchain for spiking neural network in edge computing,” Sensors, vol. 23, no. 14, p. 6548, 2023.
  197. N. Skatchkovsky, H. Jang, and O. Simeone, “Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, 2020, pp. 8524–8528.
  198. A. M. Zyarah, N. Soures, and D. Kudithipudi, “On-Device Learning in Memristor Spiking Neural Networks,” in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), May 2018, pp. 1–5, iSSN: 2379-447X.
  199. N. Soures, L. Hays, E. Bohannon, A. M. Zyarah, and D. Kudithipudi, “On-device STDP and synaptic normalization for neuromemristive spiking neural network,” in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), August 2017, pp. 1081–1084, iSSN: 1558-3899.
  200. P. G. Stratton, T. J. Hamilton, and A. Wabnitz, “Unsupervised Feature Vector Clustering Using Temporally Coded Spiking Networks,” in 2023 International Joint Conference on Neural Networks (IJCNN), June 2023, pp. 1–7, iSSN: 2161-4407.
  201. G. Tang, K. Vadivel, Y. Xu, R. Bilgic, K. Shidqi, P. Detterer, S. Traferro, M. Konijnenburg, M. Sifalakis, G.-J. van Schaik, and A. Yousefzadeh, “SENECA: building a fully digital neuromorphic processor, design trade-offs and challenges,” Frontiers in Neuroscience, vol. 17, 2023.
  202. A. Safa, J. Van Assche, M. D. Alea, F. Catthoor, and G. G. Gielen, “Neuromorphic Near-Sensor Computing: From Event-Based Sensing to Edge Learning,” IEEE Micro, vol. 42, no. 6, pp. 88–95, November 2022, conference Name: IEEE Micro.
  203. G. Hinton, “The Forward-Forward Algorithm: Some Preliminary Investigations,” December 2022, arXiv:2212.13345 [cs].
  204. F. De Vita, R. M. A. Nawaiseh, D. Bruneo, V. Tomaselli, M. Lattuada, and M. Falchetto, “µ-FF: On-Device Forward-Forward Training Algorithm for Microcontrollers,” in 2023 IEEE International Conference on Smart Computing (SMARTCOMP), June 2023, pp. 49–56, iSSN: 2693-8340.
  205. D. P. Pau and F. M. Aymone, “Suitability of forward-forward and pepita learning to mlcommons-tiny benchmarks,” in 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS).   IEEE, 2023, pp. 1–6.
  206. G. Dellaferrera and G. Kreiman, “Error-driven input modulation: Solving the credit assignment problem without a backward pass,” in Proceedings of the 39th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, Eds., vol. 162.   PMLR, 17–23 Jul 2022, pp. 4937–4955.
  207. T. Rahman, A. Wheeldon, R. Shafik, A. Yakovlev, J. Lei, O.-C. Granmo, and S. Das, “Data Booleanization for Energy Efficient On-Chip Learning using Logic Driven AI,” in Proceedings - 2022 International Symposium on the Tsetlin Machine, ISTM 2022, 2022, pp. 29–36.
  208. C. Profentzas, M. Almgren, and O. Landsiedel, “MiniLearn: On-Device Learning for Low-Power IoT Devices,” in International Conference on Embedded Wireless Systems and Networks, 2022, iSSN: 2562-2331.
  209. A. Carta, G. Carfì, V. De Caro, and C. Gallicchio, “Efficient Anomaly Detection on Temporal Data via Echo State Networks and Dynamic Thresholding,” in CEUR Workshop Proceedings, vol. 3350, 2023, pp. 56–67.
  210. D. Nadalini, M. Rusci, L. Benini, and F. Conti, “Reduced precision floating-point optimization for Deep Neural Network On-Device Learning on microcontrollers,” Future Generation Computer Systems, vol. 149, pp. 212–226, 2023.
  211. S. G. Patil, P. Jain, P. Dutta, I. Stoica, and J. Gonzalez, “POET: Training neural networks on tiny devices with integrated rematerialization and paging,” in Proceedings of the 39th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 162.   PMLR, 17–23 Jul 2022, pp. 17 573–17 583.
  212. Y. Choukroun, E. Kravchik, F. Yang, and P. Kisilev, “Low-bit quantization of neural networks for efficient inference,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019, pp. 3009–3018.
  213. M. Mohsin and D. Perera, “An FPGA-based hardware accelerator for k-nearest neighbor classification for machine learning on mobile devices,” in ACM International Conference Proceeding Series, 2018.
  214. J. Yang, Y. Sheng, Y. Zhang, W. Jiang, and L. Yang, “On-Device Unsupervised Image Segmentation,” in Proceedings - Design Automation Conference, vol. 2023-July, 2023.
  215. A. Albaseer, M. Abdallah, A. Al-Fuqaha, and A. Erbad, “Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks,” in 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings, 2021.
  216. D. Wang, N. Zhang, and M. Tao, “Clustered federated learning with weighted model aggregation for imbalanced data,” China Communications, pp. 41–56, 2022.
  217. N. Lu, Z. Wang, X. Li, G. Niu, Q. Dou, and M. Sugiyama, “Federated Learning from only Unlabeled Data with Class-Conditional-Sharing Clients,” arXiv preprint arXiv:2204.03304, 2022.
  218. Y.-Y. Hsieh, Y.-C. Lee, and C.-H. Yang, “A cyclegan accelerator for unsupervised learning on mobile devices,” in Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2020-October, 2020.
  219. S. Muthu, R. Tennakoon, R. Hoseinnezhad, and A. Bab-Hadiashar, “Unsupervised video object segmentation: an affinity and edge learning approach,” International Journal of Machine Learning and Cybernetics, 2022.
  220. D. Piyasena, M. Thathsara, S. Kanagarajah, S. Lam, and M. Wu, “Dynamically Growing Neural Network Architecture for Lifelong Deep Learning on the Edge,” in Proceedings - 30th International Conference on Field-Programmable Logic and Applications, FPL 2020, 2020, pp. 262–268.
  221. Z. Li, Z. Chen, X. Wei, S. Gao, C. Ren, and T. Quek, “HPFL-CN: Communication-Efficient Hierarchical Personalized Federated Edge Learning via Complex Network Feature Clustering,” in Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, vol. 2022-September, 2022, pp. 325–333.
  222. B. Gong, T. Xing, Z. Liu, W. Xi, and X. Chen, “Towards Hierarchical Clustered Federated Learning with Model Stability on Mobile Devices,” IEEE Transactions on Mobile Computing, pp. 1–17, 2023.
  223. W. Zhuang, Y. Wen, and S. Zhang, “Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification,” in MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 433–441.
  224. K. Sivamayil, E. Rajasekar, B. Aljafari, S. Nikolovski, S. Vairavasundaram, and I. Vairavasundaram, “A systematic study on reinforcement learning based applications,” Energies, vol. 16, no. 3, 2023.
  225. M. Naeem, S. T. H. Rizvi, and A. Coronato, “A gentle introduction to reinforcement learning and its application in different fields,” IEEE Access, vol. 8, pp. 209 320–209 344, 2020.
  226. S.-C. Kao and T. Krishna, “E3: A HW/SW Co-design Neuroevolution Platform for Autonomous Learning in Edge Device,” in Proceedings - 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021, 2021, pp. 288–298.
  227. H. H. Zhuo, W. Feng, Y. Lin, Q. Xu, and Q. Yang, “Federated deep reinforcement learning,” 2020.
  228. J. Qi, Q. Zhou, L. Lei, and K. Zheng, “Federated reinforcement learning: techniques, applications, and open challenges,” Intelligence & Robotics, 2021.
  229. Y. Xianjia, J. Queralta, J. Heikkonen, and T. Westerlund, “Federated Learning in Robotic and Autonomous Systems,” in Procedia Computer Science, vol. 191, 2021, pp. 135–142.
  230. C. Nadiger, A. Kumar, and S. Abdelhak, “Federated reinforcement learning for fast personalization,” in 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2019, Conference paper, p. 123 – 127, cited by: 49.
  231. W. Xiong, Q. Liu, F. Li, B. Wang, and F. Zhu, “Personalized federated reinforcement learning: Balancing personalization and experience sharing via distance constraint[formula presented],” Expert Systems with Applications, vol. 238, 2024, cited by: 0.
  232. A. Jarwan and M. Ibnkahla, “Edge-Based Federated Deep Reinforcement Learning for IoT Traffic Management,” IEEE Internet of Things Journal, vol. 10, no. 5, pp. 3799–3813, 2023.
  233. X. Wang, C. Wang, X. Li, V. C. M. Leung, and T. Taleb, “Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9441–9455, 2020.
  234. T. Liu, T. Zhang, J. Loo, and Y. Wang, “Deep Reinforcement Learning-Based Resource Allocation for UAV-Enabled Federated Edge Learning,” Journal of Communications and Information Networks, vol. 8, no. 1, 2023.
  235. G. Rjoub, O. Wahab, J. Bentahar, and A. Bataineh, “Trust-driven reinforcement selection strategy for federated learning on IoT devices,” Computing, 2022.
  236. P. Tam, I. Song, S. Kang, and S. Kim, “Privacy-Aware Intelligent Healthcare Services with Federated Learning Architecture and Reinforcement Learning Agent,” in Lecture Notes in Electrical Engineering, vol. 1028 LNEE, 2023, pp. 583–590.
  237. M. Xu, D. Niyato, Z. Yang, Z. Xiong, J. Kang, D. Kim, and X. Shen, “Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet,” IEEE Journal on Selected Topics in Signal Processing, vol. 17, no. 1, pp. 142–157, 2023.
  238. C. Peng, Q. Hu, Z. Wang, R. Liu, and Z. Xiong, “Online-Learning-Based Fast-Convergent and Energy-Efficient Device Selection in Federated Edge Learning,” IEEE Internet of Things Journal, vol. 10, no. 6, pp. 5571–5582, 2023.
  239. D. Zhang, W. Sun, Z.-A. Zheng, W. Chen, and S. He, “Adaptive device sampling and deadline determination for cloud-based heterogeneous federated learning,” Journal of Cloud Computing, vol. 12, no. 1, 2023.
  240. N. Zhao, Y. Pei, Y.-C. Liang, and D. Niyato, “Multi-Agent Deep Reinforcement Learning Based Incentive Mechanism for Multi-Task Federated Edge Learning,” IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 13 530–13 535, 2023.
  241. H. Watanabe, M. Tsukada, and H. Matsutani, “An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning,” in 2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021, 2021, pp. 96–103.
  242. K. Rakesh, L. Kumar, R. Mittar, P. Chakraborty, P. Ankush, and S. Gairuboina, “DNN based adaptive video streaming using combination of supervised learning and reinforcement learning,” in Communications in Computer and Information Science, vol. 1148 CCIS, 2020, pp. 143–154.
  243. H. Zhang, A. Zhou, and H. Ma, “Reinforcement learning-based real-time video streaming control and on-device training research,” Chinese Journal on Internet of Things, vol. 6, no. 4, pp. 1–13, 2022.
  244. S.-S. Park, D.-H. Kim, J.-G. Kang, and K.-S. Chung, “EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning,” in 2021 18th International SoC Design Conference (ISOCC), October 2021, pp. 235–236, iSSN: 2163-9612.
  245. M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu, “Safe reinforcement learning via shielding,” CoRR, vol. abs/1708.08611, 2017.
  246. T. Sen and H. Shen, “Distributed Training for Deep Learning Models On An Edge Computing Network Using Shielded Reinforcement Learning,” in Proceedings - International Conference on Distributed Computing Systems, vol. 2022-July, 2022, pp. 581–591.
  247. J. E. Van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Machine learning, vol. 109, no. 2, pp. 373–440, 2020.
  248. J. Park, J. Kwak, K. Kim, S.-S. Lee, and S.-J. Jang, “Semi-Supervised Learning using Sequential Data for Mobile Applications,” in 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022, 2022.
  249. X. Pei, X. Deng, S. Tian, L. Zhang, and K. Xue, “A Knowledge Transfer-Based Semi-Supervised Federated Learning for IoT Malware Detection,” IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 3, pp. 2127–2143, 2023.
  250. A. Albaseer, B. Ciftler, M. Abdallah, and A. Al-Fuqaha, “Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning,” in 2020 International Wireless Communications and Mobile Computing, IWCMC 2020, 2020, pp. 1666–1671.
  251. M. Tsukada, M. Kondo, and H. Matsutani, “A neural network-based on-device learning anomaly detector for edge devices,” IEEE Transactions on Computers, vol. 69, no. 7, pp. 1027–1044, 2020.
  252. S. Zhao, D. Wu, J. Yang, and M. Sawan, “A Resource-Efficient and Data-Restricted Training Method Towards Neurological Symptoms Prediction,” in BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings, 2022, pp. 615–619.
  253. D. Hou, R. Hou, and J. Hou, “On-device Training for Breast Ultrasound Image Classification,” in 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020, 2020, pp. 78–82.
  254. M. Wu, H. Matsutani, and M. Kondo, “ONLAD-IDS: ONLAD-Based Intrusion Detection System Using SmartNIC,” in Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022, 2022, pp. 546–553.
  255. V. Radu, P. Katsikouli, R. Sarkar, and M. Marina, “A semi-supervised learning approach for robust indoor-outdoor detection with smartphones,” in SenSys 2014 - Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems, 2014, pp. 280–294.
  256. R. Balestriero, M. Ibrahim, V. Sobal, A. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian et al., “A cookbook of self-supervised learning,” arXiv preprint arXiv:2304.12210, 2023.
  257. Y. Gaol, J. Fernandez-Marques, T. Parcollet, P. De Gusmao, and N. Lane, “Match to Win: Analysing Sequences Lengths for Efficient Self-Supervised Learning in Speech and Audio,” in 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings, 2023, pp. 115–122.
  258. Z. Huo, D. Hwang, K. Sim, S. Garg, A. Misra, N. Siddhartha, T. Strohman, and F. Beaufays, “Incremental Layer-Wise Self-Supervised Learning for Efficient Unsupervised Speech Domain Adaptation On Device,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2022-September, 2022, pp. 4845–4849.
  259. J. Liu, X. Yu, and T. Rosing, “Self-Train: Self-Supervised On-Device Training for Post-Deployment Adaptation,” in Proceedings - 2022 IEEE International Conference on Smart Internet of Things, SmartIoT 2022, 2022, pp. 161–168.
  260. J. Shi, Y. Wu, D. Zeng, J. Tao, J. Hu, and Y. Shi, “Self-Supervised On-Device Federated Learning From Unlabeled Streams,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 12, pp. 4871–4882, 2023.
  261. Y. Wu, Z. Wang, D. Zeng, Y. Shi, and J. Hu, “Enabling On-Device Self-Supervised Contrastive Learning with Selective Data Contrast,” in Proceedings - Design Automation Conference, vol. 2021-December, 2021, pp. 655–660.
  262. Y. Wu, D. Zeng, Z. Wang, Y. Sheng, L. Yang, A. James, Y. Shi, and J. Hu, “Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning,” in IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, vol. 2021-November, 2021.
  263. F. Kitsios, M. Kamariotou, A. I. Syngelakis, and M. A. Talias, “Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review,” Applied Sciences, vol. 13, no. 13, p. 7479, January 2023, number: 13 Publisher: Multidisciplinary Digital Publishing Institute.
  264. A. Qayyum, J. Qadir, M. Bilal, and A. I. Al-Fuqaha, “Secure and robust machine learning for healthcare: A survey,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 156–180, 2020.
  265. A. Zainuddin, “Artificial Intelligence and Machine learning in the Healthcare Sector: A Review,” Malaysian Journal of Science and Advanced Technology, July 2021.
  266. E. Petersen, Y. Potdevin, E. Mohammadi, S. Zidowitz, S. Breyer, D. Nowotka, S. Henn, L. Pechmann, M. Leucker, P. Rostalski, and C. Herzog, “Responsible and regulatory conform machine learning for medicine: A survey of challenges and solutions,” IEEE Access, vol. 10, p. 58375–58418, 2022.
  267. A. Qayyum, K. Ahmad, M. A. Ahsan, A. Al-Fuqaha, and J. Qadir, “Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge,” IEEE Open Journal of the Computer Society, vol. 3, pp. 172–184, 2022, conference Name: IEEE Open Journal of the Computer Society.
  268. Z. Lian, Q. Yang, W. Wang, Q. Zeng, M. Alazab, H. Zhao, and C. Su, “DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3558–3569, September 2022, conference Name: IEEE Transactions on Network Science and Engineering.
  269. Y. Guo, F. Liu, Z. Cai, L. Chen, and N. Xiao, “FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare,” in Proceedings of the 49th International Conference on Parallel Processing, ser. ICPP ’20.   New York, NY, USA: Association for Computing Machinery, August 2020, pp. 1–11.
  270. P. Kulkarni, H. Kasyap, and S. Tripathy, “DNet: An efficient privacy-preserving distributed learning framework for healthcare systems,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12582 LNCS, 2021, pp. 145–159.
  271. J. Chen, Y. Zheng, Y. Liang, Z. Zhan, M. Jiang, X. Zhang, D. Da Silva, W. Wu, and V. De Albuquerque, “Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 12, pp. 5772–5782, 2022.
  272. V. Chandrika and S. Surendran, “Incremental Machine Learning Model for Fetal Health Risk Prediction,” in 2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022, 2022.
  273. T. Ravi Shanker Reddy and B. Beena, “AI Integrated Blockchain Technology for Secure Health Care—Consent-Based Secured Federated Transfer Learning for Predicting COVID-19 on Wearable Devices,” in Lecture Notes in Networks and Systems, vol. 473, 2023, pp. 345–356.
  274. M. M. Kamruzzaman, “New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Smart Cities,” in 2021 IEEE Globecom Workshops (GC Wkshps), December 2021, pp. 1–6.
  275. A. M. Hayajneh, S. A. Aldalahmeh, F. Alasali, H. Al-Obiedollah, S. A. Zaidi, and D. McLernon, “Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming,” IET Smart Cities, 2023, type: Article.
  276. B. Nour, S. Cherkaoui, and Z. Mlika, “Federated Learning and Proactive Computation Reuse at the Edge of Smart Homes,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3045 – 3056, 2022, type: Article.
  277. K. Rajamohan, S. Rangasamy, A. Abreo, R. Upadhyay, and R. Sabu, “Smart cities: Redefining urban life through iot,” Advances in systems analysis, software engineering, and high performance computing book series, 2023.
  278. J. Na, H. Zhang, X. Deng, B. Zhang, and Z. Ye, “Accelerate personalized iot service provision by cloud-aided edge reinforcement learning: A case study on smart lighting,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12571 LNCS, 2020, pp. 69–84.
  279. J. M. Aguiar-Perez and M. A. Perez-Juarez, “An insight of deep learning based demand forecasting in smart grids,” Sensors, vol. 23, no. 3, 2023.
  280. J. Jithish, B. Alangot, N. Mahalingam, and K. S. Yeo, “Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach,” IEEE Access, vol. 11, pp. 7157 – 7179, 2023, type: Article.
  281. A. Taik, B. Nour, and S. Cherkaoui, “Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning,” IEEE Wireless Communications, vol. 28, no. 6, pp. 26 – 33, 2021, type: Article.
  282. W. Lei, H. Wen, J. Wu, and W. Hou, “MADDPG-based security situational awareness for smart grid with intelligent edge,” Applied Sciences (Switzerland), vol. 11, no. 7, 2021.
  283. N. N. T. Huu, L. Mai, and T. V. Minh, “Detecting Abnormal and Dangerous Activities Using Artificial Intelligence on the Edge for Smart City Application,” in Proceedings - 2021 15th International Conference on Advanced Computing and Applications, ACOMP 2021, 2021, pp. 85 – 92, type: Conference paper.
  284. C. Bian, Y. Xu, L. Wang, H. Gu, and F. Zhou, “Abnormal behavior recognition based on edge feature and 3D convolutional neural network,” in Proceedings - 2020 35th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2020, 2020, pp. 661 – 666, type: Conference paper.
  285. D. Yuan, X. Zhu, Y. Mao, B. Zheng, and T. Wu, “Privacy-Preserving Pedestrian Detection for Smart City with Edge Computing,” in 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019, 2019, type: Conference paper.
  286. S. Pandiyan and J. Rajasekharan, “Federated Learning vs Edge Learning for Hot Water Demand Forecasting in Distributed Electric Water Heaters for Demand Side Flexibility Aggregation,” in 2023 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2023, 2023.
  287. A. Jaleel, M. Hassan, T. Mahmood, M. Ghani, and A. Ur Rehman, “Reducing congestion in an intelligent traffic system with collaborative and adaptive signaling on the edge,” IEEE Access, vol. 8, pp. 205 396–205 410, 2020.
  288. G. Constantinou, G. Sankar Ramachandran, A. Alfarrarjeh, S. H. Kim, B. Krishnamachari, and C. Shahabi, “A crowd-based image learning framework using edge computing for smart city applications,” in Proceedings - 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019, 2019, pp. 11 – 20, type: Conference paper.
  289. B. Qolomany, K. Ahmad, A. Al-Fuqaha, and J. Qadir, “Particle Swarm Optimized Federated Learning for Industrial IoT and Smart City Services,” in 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings, 2020, type: Conference paper.
  290. D. Liu, E. Cui, Y. Shen, P. Ding, and Z. Zhang, “Federated Learning Model Training Mechanism with Edge Cloud Collaboration for Services in Smart Cities,” in IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, vol. 2023-June, 2023, type: Conference paper.
  291. L. Zhang, J. Wu, S. Mumtaz, J. Li, H. Gacanin, and J. J. P. C. Rodrigues, “Edge-to-edge cooperative artificial intelligence in smart cities with on-demand learning offloading,” in 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, 2019, type: Conference paper.
  292. Machine Learning Enabled Smart Farming:The Demand of the Time, 2022.
  293. I. Sharma, A. Sharma, and S. K. Gupta, “Autonomous vehicles: Open-source technologies, considerations, and development,” Advances in Artificial Intelligence and Machine Learning, pp. 105–109, 2023.
  294. B. Yang, X. Cao, X. Li, C. Yuen, and L. Qian, “Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework,” IEEE Wireless Communications Letters, vol. 9, no. 8, pp. 1182–1186, 2020.
  295. I. Sharma, A. Sharma, and S. K. Gupta, “Asynchronous and Synchronous Federated Learning-based UAVs,” in 2023 Third International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), January 2023, pp. 105–109.
  296. J. Chen, O. Esrafilian, H. Bayerlein, D. Gesbert, and M. Caccamo, “Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks,” arXiv.org, vol. abs/2306.02029, June 2023.
  297. S. Chen and K. Mai, “Towards Specialized Hardware for Learning-based Visual Odometry on the Edge,” in IEEE International Conference on Intelligent Robots and Systems, vol. 2022-October, 2022, pp. 10 603–10 610.
  298. Y. Dang, C. Benzaid, B. Yang, T. Taleb, and Y. Shen, “Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs,” IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25 068–25 085, 2022.
  299. V. Sharma, P. Saikia, S. Singh, K. Singh, W.-J. Huang, and S. Biswas, “FEEL-enhanced Edge Computing in Energy Constrained UAV-aided IoT Networks,” in IEEE Wireless Communications and Networking Conference, WCNC, vol. 2023-March, 2023.
  300. J. Liu, Z. Xu, and Z. Wen, “Joint Data Transmission and Trajectory Optimization in UAV-Enabled Wireless Powered Mobile Edge Learning Systems,” IEEE Transactions on Vehicular Technology, vol. 72, no. 9, pp. 11 617–11 630, 2023.
  301. Z. Zhao, L. Pacheco, H. Santos, M. Liu, A. Maio, D. Rosari, E. Cerqueira, T. Braun, and X. Cao, “Predictive UAV Base Station Deployment and Service Offloading with Distributed Edge Learning,” IEEE Transactions on Network and Service Management, vol. 18, no. 4, pp. 3955–3972, 2021.
  302. Y. Ding, Y. Feng, W. Lu, S. Zheng, N. Zhao, L. Meng, A. Nallanathan, and X. Yang, “Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications,” IEEE Journal on Selected Topics in Signal Processing, vol. 17, no. 1, pp. 54–65, 2023.
  303. S. Tang, W. Zhou, L. Chen, L. Lai, J. Xia, and L. Fan, “Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks,” Physical Communication, vol. 47, 2021.
  304. J. Li, X. Liu, and T. Mahmoodi, “Opportunistic Transmission of Distributed Learning Models in Mobile UAVs,” arXiv preprint arXiv:2306.09484, June 2023.
  305. G. Cappello, G. Colajanni, P. Daniele, L. Galluccio, C. Grasso, G. Schembra, and L. Scrimali, “ODEL: an On-Demand Edge-Learning framework exploiting Flying Ad-hoc NETworks (FANETs),” in Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2023, pp. 394–399.
  306. D. Selvaraj, C. Vitale, T. Panayiotou, P. Kolios, C. Chiasserini, and G. Ellinas, “Edge Learning of Vehicular Trajectories at Regulated Intersections,” in IEEE Vehicular Technology Conference, vol. 2021-September, 2021.
  307. S. Zhang, S. Zhang, and L. Yeung, “Energy-efficient Federated Edge Learning for Internet of Vehicles via Rate-Splitting Multiple Access,” in Proceedings of the International Symposium on Wireless Communication Systems, vol. 2022-October, 2022.
  308. H. Werthner, H. R. Hansen, and F. Ricci, “Recommender systems,” in 2007 40th Annual Hawaii International Conference on System Sciences (HICSS’07), vol. 1.   IEEE Computer Society, 2007, pp. 167–167.
  309. X. Xin, J. Yang, H. Wang, J. Ma, P. Ren, H. Luo, X. Shi, Z. Chen, and Z. Ren, “On the user behavior leakage from recommender system exposure,” ACM Trans. Inf. Syst., vol. 41, no. 3, feb 2023.
  310. L. Yang, B. Tan, V. Zheng, K. Chen, and Q. Yang, “Federated Recommendation Systems,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12500 LNCS, pp. 225–239, 2020.
  311. K. Muhammad, Q. Wang, D. O’Reilly-Morgan, E. Tragos, B. Smyth, N. Hurley, J. Geraci, and A. Lawlor, “FedFast: Going beyond Average for Faster Training of Federated Recommender Systems,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020, pp. 1234–1242.
  312. Z. Liu, L. Yang, Z. Fan, H. Peng, and P. Yu, “Federated Social Recommendation with Graph Neural Network,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 4, 2022.
  313. S. Wei, S. Meng, Q. Li, X. Zhou, L. Qi, and X. Xu, “Edge-enabled federated sequential recommendation with knowledge-aware Transformer,” Future Generation Computer Systems, vol. 148, pp. 610–622, 2023.
  314. S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, and M. Orgun, “Sequential recommender systems: challenges, progress and prospects,” arXiv preprint arXiv:2001.04830, 2019.
  315. F. Zhu, Y. Wang, C. Chen, J. Zhou, L. Li, and G. Liu, “Cross-domain recommendation: Challenges, progress, and prospects,” 2021.
  316. H. Hu, G. Dobbie, Z. Salcic, M. Liu, J. Zhang, L. Lyu, and X. Zhang, “Differentially private locality sensitive hashing based federated recommender system,” Concurrency and Computation: Practice and Experience, vol. 35, no. 14, p. e6233, 2023.
  317. Y. Guo, F. Liu, Z. Cai, H. Zeng, L. Chen, T. Zhou, and N. Xiao, “PREFER: Point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 5, no. 1, 2021.
  318. L. Yang, J. Zhang, D. Chai, L. Wang, K. Guo, K. Chen, and Q. Yang, “Practical and Secure Federated Recommendation with Personalized Masks,” pp. 33–45, 2023.
  319. F. Liang, W. Pan, and Z. Ming, “FedRec++: Lossless Federated Recommendation with Explicit Feedback,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4224–4231, May 2021, number: 5.
  320. Y. Du, D. Zhou, Y. Xie, J. Shi, and M. Gong, “Federated matrix factorization for privacy-preserving recommender systems,” Applied Soft Computing, vol. 111, p. 107700, November 2021.
  321. M. Dogra, B. Meher, P. Mani, and H.-K. Min, “Memory Efficient Federated Recommendation Model,” in Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022, 2022, pp. 139–142.
  322. T. Liu and Y. Sugano, “Interactive Machine Learning on Edge Devices With User-in-the-Loop Sample Recommendation,” IEEE Access, vol. 10, pp. 107 346–107 360, 2022.
  323. J. Qin, X. Zhang, B. Liu, and J. Qian, “A split-federated learning and edge-cloud based efficient and privacy-preserving large-scale item recommendation model,” Journal of Cloud Computing, vol. 12, no. 1, 2023.
  324. F. Wang, J. Liu, C. Zhang, L. Sun, and K. Hwang, “Intelligent Edge Learning for Personalized Crowdsourced Livecast: Challenges, Opportunities, and Solutions,” IEEE Network, vol. 35, no. 1, pp. 170–176, 2021.
  325. S. Karpinskyj, F. Zambetta, and L. Cavedon, “Video game personalisation techniques: A comprehensive survey,” Entertainment Computing, vol. 5, no. 4, pp. 211–218, 2014.
  326. A. Bodas, B. Upadhyay, C. Nadiger, and S. Abdelhak, “Reinforcement learning for game personalization on edge devices,” in 2018 International Conference on Information and Computer Technologies (ICICT), 2018, pp. 119–122.
  327. D. H. O. Sharan, “Advancements and future directions in human activity recognition,” International Journal For Science Technology And Engineering, 2023.
  328. M. Stojchevska, M. De Brouwer, M. Courteaux, F. Ongenae, and S. Van Hoecke, “From lab to real world: Assessing the effectiveness of human activity recognition and optimization through personalization,” Sensors, vol. 23, no. 10, 2023.
  329. C.-Y. Lin and R. Marculescu, “Model Personalization for Human Activity Recognition,” in 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), March 2020, pp. 1–7.
  330. X. Ouyang, Z. Xie, J. Zhou, G. Xing, and J. Huang, “Clusterfl: A clustering-based federated learning system for human activity recognition,” ACM Trans. Sen. Netw., vol. 19, no. 1, dec 2022.
  331. M. Craighero, D. Quarantiello, B. Rossi, D. Carrera, P. Fragneto, and G. Boracchi, “On-Device Personalization for Human Activity Recognition on STM32,” IEEE Embedded Systems Letters, pp. 1–1, 2023.
  332. A. Hard, K. Partridge, C. Nguyen, N. Subrahmanya, A. Shah, P. Zhu, I. Moreno, and R. Mathews, “Training keyword spotting models on Non-IID data with federated learning,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2020-October, 2020, pp. 4343–4347.
  333. I. Zualkernan, S. Dhou, J. Judas, A. Sajun, B. Gomez, and L. Hussain, “An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge,” Computers, vol. 11, no. 1, 2022.
  334. H. Wang, F. Li, W. Mo, P. Tao, H. Shen, Y. Wu, Y. Zhang, and F. Deng, “Novel Cloud-Edge Collaborative Detection Technique for Detecting Defects in PV Components, Based on Transfer Learning,” Energies, vol. 15, no. 21, 2022.
  335. U. Chinchole and S. Raut, “Federated Learning For Estimating Air Quality,” in 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, 2021.
  336. Y. Chen, L. Chen, C. Hong, and X. Wang, “Federated Multitask Learning with Manifold Regularization for Face Spoof Attack Detection,” Mathematical Problems in Engineering, vol. 2022, 2022.
  337. N. Soures, D. Kudithipudi, R. B. Jacobs-Gedrim, S. Agarwal, and M. Marinella, “Enabling On-Device Learning with Deep Spiking Neural Networks for Speech Recognition,” ECS Transactions, vol. 85, no. 6, p. 127, April 2018, publisher: IOP Publishing.
  338. K. Sim, A. Chandorkar, F. Gao, M. Chua, T. Munkhdalai, and F. Beaufays, “Robust continuous on-device personalization for automatic speech recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 6, 2021, pp. 4451–4455.
  339. J. Park, S. Jin, J. Park, S. Kim, D. Sandhyana, C. Lee, M. Han, J. Lee, S. Jung, C. Han, and C. Kim, “Conformer-Based on-Device Streaming Speech Recognition with KD Compression and Two-Pass Architecture,” in 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings, 2023, pp. 92–99.
  340. S. Pillay, A. MacDonald, R. Brito, H. Burd, G. O’Shea, A. Higginson, and M. Faragalli, “Federated Learning on Edge Devices in a Lunar Analogue Environment,” in International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2023-July, 2023, pp. 2006–2009.
  341. Q. Zhang, X. Che, Y. Chen, X. Ma, M. Xu, S. Dustdar, X. Liu, and S. Wang, “A Comprehensive Deep Learning Library Benchmark and Optimal Library Selection,” IEEE Transactions on Mobile Computing, pp. 1–14, 2023, conference Name: IEEE Transactions on Mobile Computing.
  342. K. Pawar, A. Khade, and B. Meswani, “On-Device Training: Efficient training on the edge with ONNX Runtime,” May 2023. [Online]. Available: https://cloudblogs.microsoft.com/opensource/2023/05/31/on-device-training-efficient-training-on-the-edge-with-onnx-runtime/
  343. “On-device training in TensorFlow Lite.” [Online]. Available: https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html
  344. D. J. Beutel, T. Topal, A. Mathur, X. Qiu, J. Fernandez-Marques, Y. Gao, L. Sani, K. H. Li, T. Parcollet, P. P. B. de Gusmão, and N. D. Lane, “Flower: A Friendly Federated Learning Research Framework,” March 2022, arXiv:2007.14390 [cs, stat].
  345. C. He, S. Li, J. So, X. Zeng, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu, X. Zhu, J. Wang, L. Shen, P. Zhao, Y. Kang, Y. Liu, R. Raskar, Q. Yang, M. Annavaram, and S. Avestimehr, “FedML: A Research Library and Benchmark for Federated Machine Learning,” July 2020.
  346. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Advances in Neural Information Processing Systems, vol. 32.   Curran Associates, Inc., 2019.
  347. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” March 2016, arXiv:1603.04467 [cs].
  348. “Pytorch Mobile.” [Online]. Available: https://pytorch.org/mobile/home/
  349. “PyTorch Edge: Enabling On-Device Inference Across Mobile and Edge Devices with ExecuTorch.” [Online]. Available: https://pytorch.org/blog/pytorch-edge/
  350. N. Kershaw and P. Pulavarthi, “ONNX Runtime | Run PyTorch models on the edge,” 2023. [Online]. Available: https://onnxruntime.ai/blogs/pytorch-on-the-edge
  351. A. Khade and K. Pawar, “On-Device Training with ONNX Runtime: A deep dive,” July 2023. [Online]. Available: https://cloudblogs.microsoft.com/opensource/2023/07/05/on-device-training-with-onnx-runtime-a-deep-dive/
  352. J. Huang, K. Pawar, A. Khade, V. Wang, and Z. Xu, “Optimum+ONNX Runtime - Easier, Faster training for your Hugging Face models,” November 2023. [Online]. Available: https://huggingface.co/blog/optimum-onnxruntime-training
  353. “TensorFlow Federated.” [Online]. Available: https://www.tensorflow.org/federated
  354. K. Burlachenko, S. Horváth, and P. Richtárik, “FL_pytorch: optimization research simulator for federated learning,” in Proceedings of the 2nd ACM International Workshop on Distributed Machine Learning, December 2021, pp. 1–7.
  355. J. H. Ro, A. T. Suresh, and K. Wu, “Fedjax: Federated learning simulation with jax,” 2021.
  356. S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečný, H. B. McMahan, V. Smith, and A. Talwalkar, “LEAF: A Benchmark for Federated Settings,” December 2019, arXiv:1812.01097 [cs, stat].
  357. D. Zeng, S. Liang, X. Hu, H. Wang, and Z. Xu, “FedLab: A Flexible Federated Learning Framework,” Journal of Machine Learning Research, vol. 24, no. 100, pp. 1–7, 2023.
  358. A. Ziller, A. Trask, A. Lopardo, B. Szymkow, B. Wagner, E. Bluemke, J.-M. Nounahon, J. Passerat-Palmbach, K. Prakash, N. Rose, T. Ryffel, Z. N. Reza, and G. Kaissis, “PySyft: A Library for Easy Federated Learning,” in Federated Learning Systems: Towards Next-Generation AI, ser. Studies in Computational Intelligence, M. H. u. Rehman and M. M. Gaber, Eds.   Cham: Springer International Publishing, 2021, pp. 111–139.
  359. A. Borthakur, A. Manna, A. Kasliwal, D. Dewan, and D. Sheet, “FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation,” Authorea Preprints, September 2023.
  360. “Personalizing a Model with On-Device Updates.” [Online]. Available: https://developer.apple.com/documentation/coreml/model_personalization/personalizing_a_model_with_on-device_updates
  361. D. Nadalini, M. Rusci, G. Tagliavini, L. Ravaglia, L. Benini, and F. Conti, “Pulp-trainlib: Enabling on-device training for risc-v multi-core mcus through performance-driven autotuning,” in International Conference on Embedded Computer Systems.   Springer, 2022, pp. 200–216.
  362. A. Aral and V. D. Maio, “Simulators and emulators for edge computing,” in Edge Computing: Models, Technologies and Applications.   IET, June 2020, pp. 291–309.
  363. M. H. Garcia, A. Manoel, D. M. Diaz, F. Mireshghallah, R. Sim, and D. Dimitriadis, “Flute: A scalable, extensible framework for high-performance federated learning simulations,” 2022.
  364. J. Liu, J. Liu, Z. Xie, X. Ning, and D. Li, “Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors,” in 6th ACM/IEEE Symposium on Edge Computing, SEC 2021, 2021, pp. 80–93.
  365. A. Lacoste, A. Luccioni, V. Schmidt, and T. Dandres, “Quantifying the carbon emissions of machine learning,” arXiv preprint arXiv:1910.09700, 2019.
  366. J. Casta textasciitilde no, S. Martínez-Fernández, X. Franch, and J. Bogner, “Exploring the carbon footprint of hugging face’s ml models: A repository mining study,” arXiv preprint arXiv:2305.11164, 2023.
  367. T. Pirson and D. Bol, “Assessing the embodied carbon footprint of iot edge devices with a bottom-up life-cycle approach,” Journal of Cleaner Production, vol. 322, p. 128966, 2021.
  368. S. Savazzi, S. Kianoush, V. Rampa, and M. Bennis, “A framework for energy and carbon footprint analysis of distributed and federated edge learning,” in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).   IEEE, 2021, pp. 1564–1569.
  369. K. Chen, H. Zhang, X. Feng, X. Zhang, B. Mi, and Z. Jin, “Backdoor attacks against distributed swarm learning,” ISA Transactions, vol. 141, pp. 59–72, October 2023.
  370. M. Ferrag, B. Kantarci, L. Cordeiro, M. Debbah, and K.-K. Choo, “Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-Enabled IoTs: An Anticipatory Study,” in 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023, 2023, pp. 1253–1258.
  371. S. Ray and J. Bhadra, “Security challenges in mobile and iot systems,” in 2016 29th IEEE International System-on-Chip Conference (SOCC), 2016, pp. 356–361.
  372. M. Nasr, N. Carlini, J. Hayase, M. Jagielski, A. F. Cooper, D. Ippolito, C. A. Choquette-Choo, E. Wallace, F. Tramèr, and K. Lee, “Scalable extraction of training data from (production) language models,” arXiv preprint arXiv:2311.17035, 2023.
  373. R. Ueda, T. Nakai, K. Yoshida, and T. Fujino, “Evaluation of Membership Inference Attack Against Federated Learning With Differential Privacy on Edge Devices,” in GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics, 2023, pp. 1161–1165.
  374. J. Yang, T. Baker, S. Gill, X. Yang, W. Han, and Y. Li, “A federated learning attack method based on edge collaboration via cloud,” Software - Practice and Experience, 2022.
  375. H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale et al., “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv:2307.09288, 2023.
  376. A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. d. l. Casas, E. B. Hanna, F. Bressand et al., “Mixtral of experts,” arXiv preprint arXiv:2401.04088, 2024.
  377. G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth et al., “Gemini: a family of highly capable multimodal models,” arXiv preprint arXiv:2312.11805, 2023.
  378. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2021.
  379. H. Liu, Q. Tian, Y. Yuan, X. Liu, X. Mei, Q. Kong, Y. Wang, W. Wang, Y. Wang, and M. D. Plumbley, “Audioldm 2: Learning holistic audio generation with self-supervised pretraining,” arXiv preprint arXiv:2308.05734, 2023.
  380. D. Ghosal, N. Majumder, A. Mehrish, and S. Poria, “Text-to-audio generation using instruction-tuned llm and latent diffusion model,” arXiv preprint arXiv:2304.13731, 2023.
  381. N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22 500–22 510.
  382. H. R. Kirk, B. Vidgen, P. Röttger, and S. A. Hale, “Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback,” arXiv preprint arXiv:2303.05453, 2023.
  383. N. Kshetri, “Cybercrime and privacy threats of large language models,” IT Professional, vol. 25, no. 3, pp. 9–13, 2023.
  384. H. Woisetschläger, A. Isenko, S. Wang, R. Mayer, and H.-A. Jacobsen, “Federated fine-tuning of llms on the very edge: The good, the bad, the ugly,” arXiv preprint arXiv:2310.03150, 2023.
  385. E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685, 2021.
  386. J. Lee-Thorp, J. Ainslie, I. Eckstein, and S. Ontanon, “Fnet: Mixing tokens with fourier transforms,” arXiv preprint arXiv:2105.03824, 2021.
  387. G. Ramakrishnan, A. Nori, H. Murfet, and P. Cameron, “Towards compliant data management systems for healthcare ml,” arXiv preprint arXiv:2011.07555, 2020.
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Authors (4)
  1. Aymen Rayane Khouas (1 paper)
  2. Mohamed Reda Bouadjenek (27 papers)
  3. Hakim Hacid (29 papers)
  4. Sunil Aryal (42 papers)
Citations (4)
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