Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
158 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Federated Computing -- Survey on Building Blocks, Extensions and Systems (2404.02779v1)

Published 3 Apr 2024 in cs.LG

Abstract: In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling collaborative processing without compromising individual data privacy. This is achieved through a decentralized network of devices, each retaining control over its data, while participating in collective computations. The motivation behind FC extends beyond technical considerations to encompass societal implications. As the need for responsible AI and ethical data practices intensifies, FC aligns with the principles of user empowerment and data sovereignty. FC comprises of Federated Learning (FL) and Federated Analytics (FA). FC systems became more complex over time and they currently lack a clear definition and taxonomy describing its moving pieces. Current surveys capture domain-specific FL use cases, describe individual components in an FC pipeline individually or decoupled from each other, or provide a quantitative overview of the number of published papers. This work surveys more than 150 papers to distill the underlying structure of FC systems with their basic building blocks, extensions, architecture, environment, and motivation. We capture FL and FA systems individually and point out unique difference between those two.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (164)
  1. COVID-19 detection using federated machine learning. PLOS ONE 16, 6 (06 2021), 1–25. https://doi.org/10.1371/journal.pone.0252573
  2. FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning. IEEE Internet of Things Journal 8, 6 (2021), 4723–4735. https://doi.org/10.1109/JIOT.2020.3028742
  3. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond. IEEE Internet of Things Journal 8, 7 (2021), 5476–5497. https://doi.org/10.1109/JIOT.2020.3030072
  4. Nadzurah Zainal Abidin and Amelia Ritahani Ismail. 2022. Federated Deep Learning for Automated Detection of Diabetic Retinopathy. In 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–5. https://doi.org/10.1109/ICCED56140.2022.10010636
  5. Federated Learning in Edge Computing: A Systematic Survey. Sensors 22, 2 (2022), 1–45. https://doi.org/10.3390/s22020450
  6. Federated Analytics: Collaborative Data Science without Data Collection. Google. https://ai.googleblog.com/2020/05/federated-analytics-collaborative-data.html Accessed December 5, 2023.
  7. Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Access 8 (2020), 140699–140725. https://doi.org/10.1109/ACCESS.2020.3013541
  8. Towards Blockchain-based Hierarchical Federated Learning for Cyber-Physical Systems. In 2022 International Balkan Conference on Communications and Networking (BalkanCom). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 46–50. https://doi.org/10.1109/BalkanCom55633.2022.9900546
  9. Federated Learning With Quantized Global Model Updates. arXiv:2006.10672 [cs.IT]
  10. Federated Learning with Personalization Layers. arXiv:1912.00818 [cs.LG]
  11. Amna Arouj and Ahmed M. Abdelmoniem. 2022. Towards energy-aware federated learning on battery-powered clients. In Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (Sydney, New South Wales, Australia) (FedEdge ’22). Association for Computing Machinery, New York, NY, USA, 7–12. https://doi.org/10.1145/3556557.3557952
  12. Automatic Discovery of Privacy-Utility Pareto Fronts. arXiv:1905.10862 [stat.ML]
  13. Modern Federated Database Systems: An Overview. SciTePress, Online, 276–283. https://doi.org/10.5220/0009795402760283
  14. Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. arXiv:2111.02356 [cs.CR]
  15. On Purpose and by Necessity: Compliance Under the GDPR. In Financial Cryptography and Data Security: 22nd International Conference, FC 2018, Nieuwpoort, Curaçao, February 26 – March 2, 2018, Revised Selected Papers (Nieuwpoort, Curaçao). Springer-Verlag, Berlin, Heidelberg, 20–37. https://doi.org/10.1007/978-3-662-58387-6_2
  16. Decentralised Learning in Federated Deployment Environments: A System-Level Survey. ACM Comput. Surv. 54, 1, Article 15 (2 2021), 38 pages. https://doi.org/10.1145/3429252
  17. Flower: A Friendly Federated Learning Research Framework. arXiv:2007.14390 [cs.LG]
  18. Towards Federated Learning at Scale: System Design. https://doi.org/10.48550/ARXIV.1902.01046
  19. A Review of Privacy-Preserving Federated Learning for the Internet-of-Things. Springer International Publishing, Cham, 21–50. https://doi.org/10.1007/978-3-030-70604-3_2
  20. LEAF: A Benchmark for Federated Settings. arXiv:1812.01097 [cs.LG]
  21. FedAT: a high-performance and communication-efficient federated learning system with asynchronous tiers. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (St. Louis, Missouri) (SC ’21). Association for Computing Machinery, New York, NY, USA, Article 60, 16 pages. https://doi.org/10.1145/3458817.3476211
  22. Amit Chaulwar and Michael Huth. 2021. Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection. arXiv:2107.13640 [cs.CR]
  23. Digital Twin for Federated Analytics Using a Bayesian Approach. IEEE Internet of Things Journal 8, 22 (2021), 16301–16312. https://doi.org/10.1109/JIOT.2021.3098692
  24. Residential Short Term Load Forecasting Based on Federated Learning. In 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–6. https://doi.org/10.1109/DTPI55838.2022.9998969
  25. Dynamic Aggregation for Heterogeneous Quantization in Federated Learning. IEEE Transactions on Wireless Communications 20, 10 (2021), 6804–6819. https://doi.org/10.1109/TWC.2021.3076613
  26. FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning. arXiv:2004.03657 [cs.LG]
  27. Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies. https://doi.org/10.48550/ARXIV.2010.01243
  28. Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies. arXiv:2010.01243 [cs.LG]
  29. A comprehensive survey on model compression and acceleration. Artif. Intell. Rev. 53, 7 (10 2020), 5113–5155. https://doi.org/10.1007/s10462-020-09816-7
  30. Federated Learning with Gaussian Differential Privacy. In Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence (Shanghai, China) (RICAI ’20). Association for Computing Machinery, New York, NY, USA, 296–301. https://doi.org/10.1145/3438872.3439097
  31. Graham Cormode and Igor L. Markov. 2021. Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics. arXiv:2108.01521 [cs.CR]
  32. FederatedNILM: A Distributed and Privacy-Preserving Framework for Non-Intrusive Load Monitoring Based on Federated Deep Learning. In 2023 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 01–08. https://doi.org/10.1109/IJCNN54540.2023.10191549
  33. FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations. https://www.microsoft.com/en-us/research/publication/flute-a-scalable-extensible-framework-for-high-performance-federated-learning-simulations/
  34. Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems. Springer International Publishing, Cham, 157–171. https://doi.org/10.1007/978-3-030-70604-3_7
  35. Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues. IEEE Open Journal of the Computer Society 1 (2020), 45–61. https://doi.org/10.1109/OJCS.2020.2992630
  36. Cynthia Dwork. 2007. An Ad Omnia Approach to Defining and Achieving Private Data Analysis. In Proceedings of the 1st ACM SIGKDD International Conference on Privacy, Security, and Trust in KDD (San Jose, CA, USA) (PinKDD’07). Springer-Verlag, Berlin, Heidelberg, 1–13.
  37. Cynthia Dwork. 2008. Differential Privacy: A Survey of Results. In Proceedings of the 5th International Conference on Theory and Applications of Models of Computation (Xi’an, China) (TAMC’08). Springer-Verlag, Berlin, Heidelberg, 1–19.
  38. Cynthia Dwork. 2011. A Firm Foundation for Private Data Analysis. Commun. ACM 54, 1 (1 2011), 86–95. https://doi.org/10.1145/1866739.1866758
  39. A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison. In 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–10. https://doi.org/10.1109/percom50583.2021.9439129
  40. Scalable federated machine learning with FEDn. arXiv preprint arXiv:2103.00148 (2021), 1–14.
  41. Cost-efficient Federated Reinforcement Learning- Based Network Routing for Wireless Networks. In 2022 IEEE Future Networks World Forum (FNWF). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 243–248. https://doi.org/10.1109/FNWF55208.2022.00050
  42. Federated K-Private Set Intersection. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 436–445. https://doi.org/10.1145/3511808.3557321
  43. Federated Analytics: A survey. (2023), 1–33. https://doi.org/10.48550/ARXIV.2302.01326
  44. David Enthoven and Zaid Al-Ars. 2021. An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies. Springer International Publishing, Cham, 173–196. https://doi.org/10.1007/978-3-030-70604-3_8
  45. Tom Everitt and Marcus Hutter. 2018. Universal Artificial Intelligence. Springer International Publishing, Cham, 15–46. https://doi.org/10.1007/978-3-319-64816-3_2
  46. Model elasticity for hardware heterogeneity in federated learning systems. In Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (Sydney, New South Wales, Australia) (FedEdge ’22). Association for Computing Machinery, New York, NY, USA, 19–24. https://doi.org/10.1145/3556557.3557954
  47. Federated Learning Research: Trends and Bibliometric Analysis. Springer International Publishing, Cham, 1–19. https://doi.org/10.1007/978-3-030-70604-3_1
  48. Federated learning for COVID-19 screening from Chest X-ray images. Applied Soft Computing 106 (2021), 107330. https://doi.org/10.1016/j.asoc.2021.107330
  49. Pietro Ferrara and Fausto Spoto. 2018. Static Analysis for GDPR Compliance. In Italian Conference on Cybersecurity. 1–10. https://api.semanticscholar.org/CorpusID:39745243
  50. OpenFL: the open federated learning library. Physics in Medicine and Biology 67, 21 (10 2022), 214001. https://doi.org/10.1088/1361-6560/ac97d9
  51. Python Software Foundation. 2023. pickle — Python object serialization. https://docs.python.org/3/library/pickle.html Accessed March 20, 2023.
  52. Keith Frankish and William M. Ramsey. 2014. The Cambridge handbook of artificial intelligence. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9781139046855
  53. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nature Communications 12, 1 (11 10 2021), 5910. https://doi.org/10.1038/s41467-021-25972-y
  54. Mathieu N Galtier and Camille Marini. 2019. Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning. https://doi.org/10.48550/ARXIV.1910.11567
  55. Melike Gecer and Benoit Garbinato. 2024. Federated Learning for Mobility Applications. ACM Comput. Surv. 56, 5, Article 133 (1 2024), 28 pages. https://doi.org/10.1145/3637868
  56. Toward privacy-aware federated analytics of cohorts for smart mobility. Frontiers in Computer Science 4 (07 2022), 891206. https://doi.org/10.3389/fcomp.2022.891206
  57. WeBank AI Group. 2018. Federated Learning White Paper V1.0.
  58. Christoph Gröger. 2021. There is No AI without Data. Commun. ACM 64, 11 (10 2021), 98–108. https://doi.org/10.1145/3448247
  59. Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations. International Journal on Advances in Software 10 (07 2017), 1–20.
  60. Towards Federated Learning for HVAC Analytics: A Measurement Study. In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (Virtual Event, Australia) (e-Energy ’20). Association for Computing Machinery, New York, NY, USA, 68–73. https://doi.org/10.1145/3396851.3397717
  61. Comparing quality of breast cancer care in the Netherlands and Norway by federated propensity score analytics. Breast cancer research and treatment 201 (06 2023). https://doi.org/10.1007/s10549-023-06986-0
  62. FedML: A Research Library and Benchmark for Federated Machine Learning. https://doi.org/10.48550/ARXIV.2007.13518
  63. GFL: A Decentralized Federated Learning Framework Based On Blockchain. https://doi.org/10.48550/ARXIV.2010.10996
  64. Data-centric Artificial Intelligence. https://doi.org/10.48550/ARXIV.2212.11854
  65. Model Pruning Enables Efficient Federated Learning on Edge Devices. IEEE Transactions on Neural Networks and Learning Systems 34, 12 (2023), 10374–10386. https://doi.org/10.1109/TNNLS.2022.3166101
  66. FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 767–779. https://doi.org/10.1109/ICDE53745.2022.00062
  67. Advances and Open Problems in Federated Learning. 14, 1–2 (6 2021), 1–210. https://doi.org/10.1561/2200000083
  68. Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks. In 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). 224–231. https://doi.org/10.1109/FMEC59375.2023.10305961
  69. Differentially Private Multi-Site Treatment Effect Estimation. arXiv:2310.06237 [cs.LG]
  70. An Evaluation of Federated Learning Techniques for Secure and Privacy-Preserving Machine Learning on Medical Datasets. In 2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–7. https://doi.org/10.1109/AIPR57179.2022.10092212
  71. Federated split GANs. In Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (Sydney, New South Wales, Australia) (FedEdge ’22). Association for Computing Machinery, New York, NY, USA, 25–30. https://doi.org/10.1145/3556557.3557953
  72. Yogesh Kumar and Ruchi Singla. 2021. Federated Learning Systems for Healthcare: Perspective and Recent Progress. Springer International Publishing, Cham, 141–156. https://doi.org/10.1007/978-3-030-70604-3_6
  73. Weimin Lai and Qiao Yan. 2022. Federated Learning for Detecting COVID-19 in Chest CT Images: A Lightweight Federated Learning Approach. In 2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 146–149. https://doi.org/10.1109/ICFTIC57696.2022.10075165
  74. Sangyoon Lee and Dae-Hyun Choi. 2022. Federated Reinforcement Learning for Energy Management of Multiple Smart Homes With Distributed Energy Resources. IEEE Transactions on Industrial Informatics 18, 1 (2022), 488–497. https://doi.org/10.1109/TII.2020.3035451
  75. A Survey on federated learning. In 2020 IEEE 16th International Conference on Control and Automation (ICCA). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 791–796. https://doi.org/10.1109/ICCA51439.2020.9264412
  76. A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering (2021), 1–1. https://doi.org/10.1109/tkde.2021.3124599
  77. Energy Disaggregation with Federated and Transfer Learning. In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 698–703. https://doi.org/10.1109/WF-IoT51360.2021.9595167
  78. FedDANE: A Federated Newton-Type Method. arXiv:2001.01920 [cs.LG]
  79. Federated Optimization in Heterogeneous Networks. arXiv:1812.06127 [cs.LG]
  80. Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing. IEEE Transactions on Vehicular Technology 71, 5 (2022), 4692–4705. https://doi.org/10.1109/TVT.2022.3150806
  81. Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach. IEEE Internet of Things Journal 7, 8 (2020), 7751–7763. https://doi.org/10.1109/JIOT.2020.2991401
  82. A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective. ACM Comput. Surv. 54, 5, Article 95 (5 2021), 39 pages. https://doi.org/10.1145/3450288
  83. IBM Federated Learning: an Enterprise Framework White Paper V0.1. arXiv:2007.10987 [cs.LG]
  84. FEDZIP: A Compression Framework for Communication-Efficient Federated Learning. arXiv:2102.01593 [cs.LG]
  85. Arboretum: A Planner for Large-Scale Federated Analytics with Differential Privacy. In Proceedings of the 29th Symposium on Operating Systems Principles (¡conf-loc¿, ¡city¿Koblenz¡/city¿, ¡country¿Germany¡/country¿, ¡/conf-loc¿) (SOSP ’23). Association for Computing Machinery, New York, NY, USA, 451–465. https://doi.org/10.1145/3600006.3624566
  86. Communication-Efficient Learning of Deep Networks from Decentralized Data. (2016), 1–11. https://doi.org/10.48550/ARXIV.1602.05629
  87. Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv:1602.05629 [cs.LG]
  88. Xiaopeng Mo and Jie Xu. 2021. Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design. Journal of Communications and Information Networks 6, 2 (2021), 110–124. https://doi.org/10.23919/JCIN.2021.9475121
  89. Federated Alternate Training (Fat): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–5. https://doi.org/10.1109/ISBI53787.2023.10230533
  90. Where You Are Is Who You Are: User Identification by Matching Statistics. IEEE Transactions on Information Forensics and Security 11, 2 (2016), 358–372. https://doi.org/10.1109/TIFS.2015.2498131
  91. Arvind Narayanan and Vitaly Shmatikov. 2006. How To Break Anonymity of the Netflix Prize Dataset. https://doi.org/10.48550/ARXIV.CS/0610105
  92. Takayuki Nishio and Ryo Yonetani. 2019. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–7. https://doi.org/10.1109/ICC.2019.8761315
  93. State of California Department of Justice. 2018. California Consumer Privacy Act of 2018 [1798.100 - 1798.199.100].
  94. Adaptive Client Model Update with Reinforcement Learning in Synchronous Federated Learning. In 2022 32nd International Telecommunication Networks and Applications Conference (ITNAC). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–3. https://doi.org/10.1109/ITNAC55475.2022.9998360
  95. Edge-Assisted Democratized Learning Toward Federated Analytics. IEEE Internet of Things Journal 9, 1 (2022), 572–588. https://doi.org/10.1109/JIOT.2021.3085429
  96. Scalable Private Learning with PATE. arXiv:1802.08908 [stat.ML]
  97. Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey. ACM Comput. Surv. 55, 14s, Article 334 (7 2023), 27 pages. https://doi.org/10.1145/3596907
  98. Efficient Hyperparameter Optimization for Differentially Private Deep Learning. arXiv:2108.03888 [cs.LG]
  99. Cheng Qiu. 2023. A Network Traffic Classification Method Based on Federated Learning and Extreme Learning Machine. In 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 284–289. https://doi.org/10.1109/ICCECT57938.2023.10140851
  100. Can Federated Learning Save The Planet? arXiv:2010.06537 [cs.LG]
  101. Cloud-Based Federated Learning Implementation Across Medical Centers. JCO Clinical Cancer Informatics 5 (2021), 1–11. https://doi.org/10.1200/CCI.20.00060 arXiv:https://doi.org/10.1200/CCI.20.00060 PMID: 33411624.
  102. G. Pradeep Reddy and Y. V. Pavan Kumar. 2023. A Beginner’s Guide to Federated Learning. In 2023 Intelligent Methods, Systems, and Applications (IMSA). 557–562. https://doi.org/10.1109/IMSA58542.2023.10217383
  103. FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 108), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 2021–2031. https://proceedings.mlr.press/v108/reisizadeh20a.html
  104. A reflection on privacy and data confidentiality in Official Statistics. (02 2020), 1–8.
  105. The future of digital health with federated learning. npj Digital Medicine 3, 1 (14 9 2020), 119. https://doi.org/10.1038/s41746-020-00323-1
  106. Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications (2019), 1–9. https://doi.org/10.1038/s41467-019-10933-3
  107. An open-source federated learning framework. https://fedbiomed.gitlabpages.inria.fr/.
  108. A generic framework for privacy preserving deep learning. arXiv:1811.04017 [cs.LG]
  109. APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning. arXiv:2202.03672 [cs.LG]
  110. Energy Demand Prediction with Federated Learning for Electric Vehicle Networks. In 2019 IEEE Global Communications Conference (GLOBECOM). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9013587
  111. Federated learning improves site performance in multicenter deep learning without data sharing. Journal of the American Medical Informatics Association : JAMIA 28 (2021), 1259 – 1264. https://api.semanticscholar.org/CorpusID:231803993
  112. Federated office plug-load identification for building management systems. In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (Virtual Event) (e-Energy ’22). Association for Computing Machinery, New York, NY, USA, 114–126. https://doi.org/10.1145/3538637.3538845
  113. Federated Office Plug-Load Identification for Building Management Systems. In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (Virtual Event) (e-Energy ’22). Association for Computing Machinery, New York, NY, USA, 114–126. https://doi.org/10.1145/3538637.3538845
  114. Energy vs Privacy: Estimating the Ecological Impact of Federated Learning. In Proceedings of the 14th ACM International Conference on Future Energy Systems (Orlando, FL, USA) (e-Energy ’23). Association for Computing Machinery, New York, NY, USA, 347–352. https://doi.org/10.1145/3575813.3597344
  115. Amit P. Sheth and James A. Larson. 1990. Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases. ACM Comput. Surv. 22, 3 (9 1990), 183–236. https://doi.org/10.1145/96602.96604
  116. Federated Anomaly Analytics for Local Model Poisoning Attack. IEEE Journal on Selected Areas in Communications 40, 2 (2022), 596–610. https://doi.org/10.1109/JSAC.2021.3118347
  117. Federated Learning with Quantization Constraints. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers (IEEE), Barcelona, Spain, 8851–8855. https://doi.org/10.1109/ICASSP40776.2020.9054168
  118. UVeQFed: Universal Vector Quantization for Federated Learning. IEEE Transactions on Signal Processing 69 (2021), 500–514. https://doi.org/10.1109/TSP.2020.3046971
  119. A field test of a federated learning/federated analytic blockchain network implementation in an HPC environment. In Frontiers in Blockchain, Vol. 5. Frontiers in Blockchain, 1–9. https://doi.org/10.3389/fbloc.2022.893747
  120. Personal Data Protection Commission Singapore. 2014. Personal Data Protection Act.
  121. An Improved Infrastructure for Privacy-Preserving Analysis of Patient Data. Studies in health technology and informatics 295 (6 2022), 144–147. https://doi.org/10.3233/SHTI220682
  122. Johannes Stutz. 2021. Limitations of Information Flows. https://blog.openmined.org/limitations-of-information-flows/ Accessed March 16, 2023.
  123. Afaf Taik and Soumaya Cherkaoui. 2020. Electrical Load Forecasting Using Edge Computing and Federated Learning. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–6. https://doi.org/10.1109/ICC40277.2020.9148937
  124. DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. arXiv:1905.05957 [cs.DC]
  125. Federated Learning Team. 2021. Federated Learning for Healthcare Using NVIDIA Clara White Paper.
  126. Scalable and Modular AI Deployment Powered by NVIDIA Clara Deploy White Paper.
  127. Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning. Springer International Publishing, Cham, 79–109. https://doi.org/10.1007/978-3-030-70604-3_4
  128. Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning. IEEE Open Journal of Vehicular Technology 2 (2021), 151–161. https://doi.org/10.1109/OJVT.2021.3065529
  129. 5G on the Roads: Latency-Optimized Federated Analytics in the Vehicular Edge. IEEE Access 11 (2023), 81737–81752. https://doi.org/10.1109/ACCESS.2023.3301330
  130. Beyond Privacy Trade-offs with Structured Transparency. https://doi.org/10.48550/ARXIV.2012.08347
  131. Federated Learning based Energy Demand Prediction with Clustered Aggregation. In 2021 IEEE International Conference on Big Data and Smart Computing (BigComp). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 164–167. https://doi.org/10.1109/BigComp51126.2021.00039
  132. European Union. 2016. REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation).
  133. Joel Walmsley. 2012. Classical Cognitive Science and Good Old Fashioned AI. Palgrave Macmillan UK, London, 30–64. https://doi.org/10.1057/9781137283429_3
  134. Federated Analytics: Opportunities and Challenges. IEEE Network 36, 1 (2022), 151–158. https://doi.org/10.1109/MNET.101.2100328
  135. Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection. ArXiv abs/2105.11085 (2021), 51–60. https://api.semanticscholar.org/CorpusID:235166219
  136. Federated Learning with Matched Averaging. arXiv:2002.06440 [cs.LG]
  137. Federated Learning for Energy-Efficient Task Computing in Wireless Networks. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–6. https://doi.org/10.1109/ICC40277.2020.9148625
  138. Ting Wang and Ling Liu. 2011. Output privacy in data mining. ACM Trans. Database Syst. 36, 1, Article 1 (3 2011), 34 pages. https://doi.org/10.1145/1929934.1929935
  139. FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining. In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 61–70. https://doi.org/10.1109/INFOCOM48880.2022.9796719
  140. Federated Analytics Informed Distributed Industrial IoT Learning With Non-IID Data. IEEE Transactions on Network Science and Engineering 10, 5 (2023), 2924–2939. https://doi.org/10.1109/TNSE.2022.3187992
  141. Secure Trajectory Publication in Untrusted Environments: A Federated Analytics Approach. IEEE Transactions on Mobile Computing 22, 11 (2023), 6742–6754. https://doi.org/10.1109/TMC.2022.3198550
  142. Differentially Private Federated Learning: Algorithm, Analysis and Optimization. Springer International Publishing, Cham, 51–78. https://doi.org/10.1007/978-3-030-70604-3_3
  143. A survey of federated learning for edge computing: Research problems and solutions. High-Confidence Computing 1, 1 (2021), 100008. https://doi.org/10.1016/j.hcc.2021.100008
  144. FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. arXiv:2204.05011 [cs.LG]
  145. Federated Learning for Healthcare Informatics. Springer. https://doi.org/10.1007/s41666-020-00082-4
  146. Jie Xu and Heqiang Wang. 2021. Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective. IEEE Transactions on Wireless Communications 20, 2 (2021), 1188–1200. https://doi.org/10.1109/TWC.2020.3031503
  147. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Medical Image Analysis 70 (2021), 101992. https://doi.org/10.1016/j.media.2021.101992
  148. Federated Machine Learning: Concept and Applications. ACM Trans. Intell. Syst. Technol. 10, 2, Article 12 (1 2019), 19 pages. https://doi.org/10.1145/3298981
  149. Privacy is not Free: Energy-Aware Federated Learning for Mobile and Edge Intelligence. In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 233–238. https://doi.org/10.1109/WCSP49889.2020.9299703
  150. Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach. IEEE Access 8 (2020), 23920–23935. https://doi.org/10.1109/ACCESS.2020.2968399
  151. A Comprehensive Survey of Privacy-Preserving Federated Learning: A Taxonomy, Review, and Future Directions. ACM Comput. Surv. 54, 6, Article 131 (7 2021), 36 pages. https://doi.org/10.1145/3460427
  152. Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient. arXiv:2106.06921 [cs.LG]
  153. Hui Zang and Jean Bolot. 2011. Anonymization of Location Data Does Not Work: A Large-Scale Measurement Study. In Proceedings of the 17th Annual International Conference on Mobile Computing and Networking (Las Vegas, Nevada, USA) (MobiCom ’11). Association for Computing Machinery, New York, NY, USA, 145–156. https://doi.org/10.1145/2030613.2030630
  154. Energy-Efficient Radio Resource Allocation for Federated Edge Learning. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1–6. https://doi.org/10.1109/ICCWorkshops49005.2020.9145118
  155. A survey on federated learning. Knowledge-Based Systems 216 (2021), 106775. https://doi.org/10.1016/j.knosys.2021.106775
  156. Device-centric Federated Analytics At Ease. arXiv:2206.11491 [cs.DC]
  157. FedNILM: Applying Federated Learning to NILM Applications at the Edge. IEEE Transactions on Green Communications and Networking 7, 2 (2023), 857–868. https://doi.org/10.1109/TGCN.2022.3167392
  158. CrowdFA: A Privacy-Preserving Mobile Crowdsensing Paradigm via Federated Analytics. IEEE Transactions on Information Forensics and Security 18 (2023), 5416–5430. https://doi.org/10.1109/TIFS.2023.3308714
  159. Protea: client profiling within federated systems using flower. In Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (Sydney, New South Wales, Australia) (FedEdge ’22). Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.org/10.1145/3556557.3557950
  160. A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things. arXiv:2104.10501 [cs.DC]
  161. Federated Learning on Non-IID Data: A Survey. arXiv:2106.06843 [cs.LG]
  162. Federated Learning of Unsegmented Chinese Text Recognition Model. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 1341–1345. https://doi.org/10.1109/ICTAI.2019.00186
  163. FedMC: Federated Reinforcement Learning on the Edge with Meta-Critic Networks. In 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC). Institute of Electrical and Electronics Engineers (IEEE), New York, NY, USA, 344–351. https://doi.org/10.1109/IPCCC55026.2022.9894336
  164. Efficient federated learning under non-IID conditions with attackers. In Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (Sydney, New South Wales, Australia) (FedEdge ’22). Association for Computing Machinery, New York, NY, USA, 13–18. https://doi.org/10.1145/3556557.3557951

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets