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The Internet of Things in the Era of Generative AI: Vision and Challenges (2401.01923v4)

Published 3 Jan 2024 in cs.DC, cs.LG, and cs.NI

Abstract: Advancements in Generative AI hold immense promise to push Internet of Things (IoT) to the next level. In this article, we share our vision on IoT in the era of Generative AI. We discuss some of the most important applications of Generative AI in IoT-related domains. We also identify some of the most critical challenges and discuss current gaps as well as promising opportunities on enabling Generative AI for IoT. We hope this article can inspire new research on IoT in the era of Generative AI.

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References (146)
  1. Abebe Abeshu und Naveen Chilamkurti. Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2) S. 169–175, 2018.
  2. SecureBERT: A Domain-Specific Language Model for Cybersecurity. In International Conference on Security and Privacy in Communication Systems, S. 39–56. Springer, 2022.
  3. Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction. Advances in Neural Information Processing Systems, 35 S. 29677–29690, 2022.
  4. FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things. arXiv preprint arXiv:2310.00109, 2023.
  5. CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model. In 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), S. 1–8. IEEE, 2022.
  6. DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale, 2022.
  7. T. J. Anande und Mark Stephen Leeson. Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation. International Journal of Machine Learning and Computing, 2022. URL: https://api.semanticscholar.org/CorpusID:253339791.
  8. A Federated Channel Modeling System using Generative Neural Networks. 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), S. 1–5, 2023. URL: https://api.semanticscholar.org/CorpusID:258967882.
  9. Internet of robotic things: driving intelligent robotics of future-concept, architecture, applications and technologies. In 2018 4th international conference on computing sciences (ICCS), S. 151–160. IEEE, 2018.
  10. CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain. arXiv preprint arXiv:2212.02974, 2022.
  11. Matan Ben Noach und Yoav Goldberg. Compressing Pre-trained Language Models by Matrix Decomposition. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, S. 884–889, Suzhou, China, December 2020. Association for Computational Linguistics. URL: https://aclanthology.org/2020.aacl-main.88.
  12. Language Models are Few-Shot Learners. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, und H. Lin, (Hrsg.), Advances in Neural Information Processing Systems, volume 33, S. 1877–1901. Curran Associates, Inc., 2020. URL: https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf.
  13. Federico Busato und Jeff Pool. Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt. https://developer.nvidia.com/blog/exploiting-ampere-structured-sparsity-with-cusparselt, December 2020. Accessed: 2023-12-13.
  14. Enable deep learning on mobile devices: Methods, systems, and applications. ACM Transactions on Design Automation of Electronic Systems (TODAES), 27(3) S. 1–50, 2022.
  15. Shaoyu Cai und Kening Zhu. Multi-modal Transformer-based Tactile Signal Generation for Haptic Texture Simulation of Materials in Virtual and Augmented Reality. 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), S. 810–811, 2022. URL: https://api.semanticscholar.org/CorpusID:252275601.
  16. A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT, 2023.
  17. D. Chandramohan und B. V. Ramana Reddy. Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network. Transactions on Emerging Telecommunications Technologies, 34, 2023. URL: https://api.semanticscholar.org/CorpusID:256893709.
  18. Scatterbrain: Unifying Sparse and Low-rank Attention. In A. Beygelzimer, Y. Dauphin, P. Liang, und J. Wortman Vaughan, (Hrsg.), Advances in Neural Information Processing Systems, 2021. URL: https://openreview.net/forum?id=SehIKudiIo1.
  19. AlpaGasus: Training A Better Alpaca with Fewer Data, 2023.
  20. Song Chen und Hai Liao. Bert-log: Anomaly detection for system logs based on pre-trained language model. Applied Artificial Intelligence, 36(1) S. 2145642, 2022.
  21. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation, OSDI’18, S. 579–594, USA, 2018. USENIX Association. ISBN 9781931971478.
  22. Adapting Language Models to Compress Contexts, 2023.
  23. Myeon-Gyun Cho. A study on smart aging system for the elderly based on metaverse. Journal of Digital Convergence, 20(2) S. 261–268, 2022.
  24. ChatGPT vs. Lightweight Security: First Work Implementing the NIST Cryptographic Standard ASCON. arXiv preprint arXiv:2306.08178, 2023.
  25. Training Verifiers to Solve Math Word Problems, 2021.
  26. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, 2022.
  27. Kemal Davaslioglu und Yalin Evren Sagduyu. Generative Adversarial Learning for Spectrum Sensing. 2018 IEEE International Conference on Communications (ICC), S. 1–6, 2018. URL: https://api.semanticscholar.org/CorpusID:4560063.
  28. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2019.
  29. Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5 S. 220–235, 2023. doi: 10.1038/s42256-023-00626-4. URL: https://doi.org/10.1038/s42256-023-00626-4.
  30. Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. In Francisco Ruiz, Jennifer Dy, und Jan-Willem van de Meent, (Hrsg.), Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, S. 6630–6660. PMLR, 25–27 Apr 2023.
  31. Cynthia Dwork. Differential privacy: A survey of results. In International conference on theory and applications of models of computation, S. 1–19. Springer, 2008.
  32. Qiang Fan und Nirwan Ansari. Application Aware Workload Allocation for Edge Computing-Based IoT. IEEE Internet of Things Journal, 5(3) S. 2146–2153, 2018. doi: 10.1109/JIOT.2018.2826006.
  33. NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom), S. 115–127, New Delhi, India, 2018.
  34. Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters. ACM Trans. Internet Technol., 23(1), feb 2023. ISSN 1533-5399. doi: 10.1145/3551638. URL: https://doi.org/10.1145/3551638.
  35. Revolutionizing Cyber Threat Detection with Large Language Models. arXiv preprint arXiv:2306.14263, 2023.
  36. Elias Frantar und Dan Alistarh. SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot. arXiv preprint arXiv:2301.00774, 2023.
  37. GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers. arXiv preprint arXiv:2210.17323, 2022.
  38. Making Pre-trained Language Models Better Few-shot Learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), S. 3816–3830, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.295. URL: https://aclanthology.org/2021.acl-long.295.
  39. Georgi Gerganov. llama.cpp. https://github.com/ggerganov/llama.cpp, 2023.
  40. Demo: Scalable Digital Twin System for Mobile Networks with Generative AI. Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services, 2023. URL: https://api.semanticscholar.org/CorpusID:259177820.
  41. Generative Adversarial Networks, 2014.
  42. Openvino deep learning workbench: Comprehensive analysis and tuning of neural networks inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, S. 0–0, 2019.
  43. Roberto Gozalo-Brizuela und Eduardo C. Garrido-Merchán. A survey of Generative AI Applications, 2023.
  44. IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54 S. 32–47, 2014.
  45. A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models, 2023.
  46. Strategies for applying low rank decomposition to transformer-based models. In 36th Conference on Neural Information Processing Systems (NeurIPS2022), 2022.
  47. Retransmission Policies for Efficient Communication in IoT Applications. In 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), S. 197–202, 2018. doi: 10.1109/FiCloud.2018.00036.
  48. Better Late Than Never: GAN-Enhanced Dynamic Anti-Jamming Spectrum Access With Incomplete Sensing Information. IEEE Wireless Communications Letters, 10 S. 1800–1804, 2021a. URL: https://api.semanticscholar.org/CorpusID:236371972.
  49. A Dynamic Resource Allocation Framework for Synchronizing Metaverse with IoT Service and Data. ICC 2022 - IEEE International Conference on Communications, S. 1196–1201, 2021b. URL: https://api.semanticscholar.org/CorpusID:240353957.
  50. A Dynamic Hierarchical Framework for IoT-assisted Metaverse Synchronization. ArXiv, abs/2203.03969, 2022. URL: https://api.semanticscholar.org/CorpusID:247315497.
  51. A Dynamic Hierarchical Framework for IoT-Assisted Digital Twin Synchronization in the Metaverse. IEEE Internet of Things Journal, 10 S. 268–284, 2023. URL: https://api.semanticscholar.org/CorpusID:251783765.
  52. Measuring Massive Multitask Language Understanding. Proceedings of the International Conference on Learning Representations (ICLR), 2021.
  53. Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout. Advances in Neural Information Processing Systems, 34 S. 12876–12889, 2021.
  54. Parameter-Efficient Transfer Learning for NLP, 2019.
  55. LoRA: Low-Rank Adaptation of Large Language Models, 2021.
  56. Numerical Optimizations for Weighted Low-rank Estimation on Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, S. 1404–1416, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.emnlp-main.91. URL: https://aclanthology.org/2022.emnlp-main.91.
  57. AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head, 2023.
  58. Leila Ismail und Rajkumar Buyya. Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds. ArXiv, abs/2308.10559, 2023. URL: https://api.semanticscholar.org/CorpusID:261049832.
  59. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2) S. 1–210, 2021.
  60. Andreas Kamilaris und Nicolo Botteghi. The penetration of Internet of Things in robotics: Towards a web of robotic things. Journal of ambient intelligence and smart environments, 12(6) S. 491–512, 2020.
  61. Diederik P Kingma und Max Welling. Auto-Encoding Variational Bayes, 2022.
  62. DDoS in the IoT: Mirai and other botnets. Computer, 50(7) S. 80–84, 2017.
  63. Xhafer Krasniqi und Edmond Hajrizi. Use of IoT technology to drive the automotive industry from connected to full autonomous vehicles. IFAC-PapersOnLine, 49(29) S. 269–274, 2016.
  64. ImagenHub: Standardizing the evaluation of conditional image generation models, 2023.
  65. Efficient Memory Management for Large Language Model Serving with PagedAttention, 2023.
  66. MLIR: Scaling compiler infrastructure for domain specific computation. In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), S. 2–14. IEEE, 2021.
  67. What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning, 2019.
  68. Fast Inference from Transformers via Speculative Decoding, 2023.
  69. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models, 2023a.
  70. Constraint-Aware and Ranking-Distilled Token Pruning for Efficient Transformer Inference. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’23, S. 1280–1290, New York, NY, USA, 2023b. Association for Computing Machinery. ISBN 9798400701030. doi: 10.1145/3580305.3599284. URL: https://doi.org/10.1145/3580305.3599284.
  71. Xiang Lisa Li und Percy Liang. Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), S. 4582–4597, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.353. URL: https://aclanthology.org/2021.acl-long.353.
  72. Unified Demonstration Retriever for In-Context Learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), S. 4644–4668, Toronto, Canada, July 2023c. Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-long.256. URL: https://aclanthology.org/2023.acl-long.256.
  73. Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning, 2023.
  74. Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Comput. Surv., 55(9), jan 2023. ISSN 0360-0300. doi: 10.1145/3560815. URL: https://doi.org/10.1145/3560815.
  75. Towards efficient NLP: A standard evaluation and A strong baseline. arXiv preprint arXiv:2110.07038, 2021.
  76. Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning, 2023.
  77. Towards a Personality AI for Robots: Potential Colony Capacity of a Goal-Shaped Generative Personality Model When Used for Expressing Personalities via Non-Verbal Behaviour of Humanoid Robots. Frontiers in Robotics and AI, 9 S. 728776, 2022.
  78. Full Parameter Fine-tuning for Large Language Models with Limited Resources, 2023.
  79. Zhihan Lv. Generative Artificial Intelligence in the Metaverse Era. Cognitive Robotics, 2023. URL: https://api.semanticscholar.org/CorpusID:259431142.
  80. LLM-Pruner: On the Structural Pruning of Large Language Models, 2023.
  81. Fine-Tuning Language Models with Just Forward Passes, 2023.
  82. WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, S. 3317–3326, 2023.
  83. Patrick McDaniel und Stephen McLaughlin. Security and privacy challenges in the smart grid. IEEE security & privacy, 7(3) S. 75–77, 2009.
  84. Bertalan Meskó und Eric J Topol. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Medicine, 6(1) S. 120, 2023.
  85. SpecInfer: Accelerating Generative Large Language Model Serving with Speculative Inference and Token Tree Verification, 2023.
  86. Microsoft. What is a Vector Database? https://learn.microsoft.com/en-us/semantic-kernel/memories/vector-db, November 2023. Accessed: 2023-12-13.
  87. Metaverse for Digital Anti-Aging Healthcare: An Overview of Potential Use Cases Based on Artificial Intelligence, Blockchain, IoT Technologies, Its Challenges, and Future Directions. Applied Sciences, 2023. URL: https://api.semanticscholar.org/CorpusID:258269281.
  88. Orca: Progressive Learning from Complex Explanation Traces of GPT-4, 2023.
  89. Efficient large-scale language model training on gpu clusters using megatron-lm. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, S. 1–15, 2021.
  90. Point-E: A System for Generating 3D Point Clouds from Complex Prompts, 2022.
  91. Kannan Nova. Generative AI in Healthcare: Advancements in Electronic Health Records, facilitating Medical Languages, and Personalized Patient Care. Journal of Advanced Analytics in Healthcare Management, 7(1) S. 115–131, 2023.
  92. OpenAI. GPT-4 Technical Report, 2023.
  93. Distributed real-time IoT for autonomous vehicles. IEEE Transactions on Industrial Informatics, 15(2) S. 1131–1140, 2018.
  94. Language models are unsupervised multitask learners. OpenAI blog, 1(8) S. 9, 2019.
  95. Scaling Language Models: Methods, Analysis & Insights from Training Gopher, 2022.
  96. Abir Rahali und Moulay A Akhloufi. MalBERT: Malware Detection using Bidirectional Encoder Representations from Transformers. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), S. 3226–3231. IEEE, 2021.
  97. Zero-shot text-to-image generation. In International Conference on Machine Learning, S. 8821–8831. PMLR, 2021.
  98. Hierarchical Text-Conditional Image Generation with CLIP Latents, 2022.
  99. Siyu Ren und Kenny Q. Zhu. Low-Rank Prune-And-Factorize for Language Model Compression, 2023.
  100. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, S. 10684–10695, 2022.
  101. Trusted Execution Environment: What It is, and What It is Not. In 2015 IEEE Trustcom/BigDataSE/ISPA, volume 1, S. 57–64, 2015. doi: 10.1109/Trustcom.2015.357.
  102. An attack detection framework based on BERT and deep learning. IEEE Access, 10 S. 68633–68644, 2022.
  103. Or Sharir und Anima Anandkumar. Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs, 2023.
  104. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU, 2023.
  105. Alex Sherstinsky. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404 S. 132306, mar 2020. doi: 10.1016/j.physd.2019.132306. URL: https://doi.org/10.1016%2Fj.physd.2019.132306.
  106. Blockwise Parallel Decoding for Deep Autoregressive Models. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, S. 10107–10116, Red Hook, NY, USA, 2018. Curran Associates Inc.
  107. A Simple and Effective Pruning Approach for Large Language Models. arXiv preprint arXiv:2306.11695, 2023.
  108. GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), S. 134–148, Toronto, Canada, July 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-industry.15. URL: https://aclanthology.org/2023.acl-industry.15.
  109. A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots. Neural Networks, 150 S. 293–312, 2022.
  110. LLaMA: Open and Efficient Foundation Language Models, 2023a.
  111. Llama 2: Open Foundation and Fine-Tuned Chat Models, 2023b.
  112. Spyros G Tzafestas. Synergy of IoT and AI in modern society: The robotics and automation case. Robot. Autom. Eng. J, 31 S. 1–15, 2018.
  113. Sunil Vadera und Salem Ameen. Methods for Pruning Deep Neural Networks, 2021.
  114. S Venkatasubramanian. Ambulatory Monitoring of Maternal and Fetal using Deep Convolution Generative Adversarial Network for Smart Health Care IoT System. International Journal of Advanced Computer Science and Applications, 13(1), 2022.
  115. Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT. Annals of Biomedical Engineering, S. 1–3, 2023.
  116. Efficient Large Language Models: A Survey, 2023.
  117. Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference, 2023a.
  118. A field guide to federated optimization. arXiv preprint arXiv:2107.06917, 2021.
  119. Self-Instruct: Aligning Language Models with Self-Generated Instructions, 2023b.
  120. Emergent Abilities of Large Language Models, 2022.
  121. Federated dropout—A simple approach for enabling federated learning on resource constrained devices. IEEE wireless communications letters, 11(5) S. 923–927, 2022.
  122. SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. arXiv, 2022.
  123. $k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference. In The Eleventh International Conference on Learning Representations, 2023a. URL: https://openreview.net/forum?id=fe2S7736sNS.
  124. LLMCad: Fast and Scalable On-device Large Language Model Inference, 2023b.
  125. Generative AI-empowered simulation for autonomous driving in vehicular mixed reality metaverses. arXiv preprint arXiv:2302.08418, 2023c.
  126. PointLLM: Empowering Large Language Models to Understand Point Clouds. arXiv preprint arXiv:2308.16911, 2023d.
  127. Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network. Journal of Lightwave Technology, 39 S. 1322–1333, 2020. URL: https://api.semanticscholar.org/CorpusID:229248300.
  128. The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision), 2023.
  129. MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI, 2023.
  130. Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, SenSys ’21, S. 29–41, New York, NY, USA, 2021. Association for Computing Machinery. ISBN 9781450390972. doi: 10.1145/3485730.3485930. URL: https://doi.org/10.1145/3485730.3485930.
  131. Data-centric AI: Perspectives and Challenges, S. 945–948. SIAM, 2023. doi: 10.1137/1.9781611977653.ch106. URL: https://epubs.siam.org/doi/abs/10.1137/1.9781611977653.ch106.
  132. Goal-Oriented Communications for the IoT and Application to Data Compression. IEEE Internet of Things Magazine, 5(4) S. 58–63, 2022a. doi: 10.1109/IOTM.001.2200177.
  133. MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing. In Advances in Neural Information Processing Systems, 2023a.
  134. LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning, 2023b.
  135. Deep Learning in the Era of Edge Computing: Challenges and Opportunities. In Book chapter in Fog Computing: Theory and Practice, Wiley, 2020.
  136. Distributed Generative Adversarial Networks for mmWave Channel Modeling in Wireless UAV Networks. ICC 2021 - IEEE International Conference on Communications, S. 1–6, 2021. URL: https://api.semanticscholar.org/CorpusID:231986833.
  137. OPT: Open Pre-trained Transformer Language Models, 2022b.
  138. Federated learning for the internet of things: Applications, challenges, and opportunities. IEEE Internet of Things Magazine, 5(1) S. 24–29, 2022c.
  139. Gpt-fl: Generative pre-trained model-assisted federated learning. arXiv preprint arXiv:2306.02210, 2023c.
  140. LIMA: Less Is More for Alignment, 2023a.
  141. Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), S. 13234–13248, Toronto, Canada, July 2023b. Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-long.738. URL: https://aclanthology.org/2023.acl-long.738.
  142. Hulk: An energy efficiency benchmark platform for responsible natural language processing. arXiv preprint arXiv:2002.05829, 2020.
  143. Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE, 107(8) S. 1738–1762, 2019. doi: 10.1109/JPROC.2019.2918951.
  144. A Survey on Model Compression for Large Language Models, 2023.
  145. IoT Edge Caching: Taxonomy, Use Cases and Perspectives. IEEE Internet of Things Magazine, 5(3) S. 12–18, 2022. doi: 10.1109/IOTM.001.2200112.
  146. Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling, 2023.
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Authors (7)
  1. Xin Wang (1306 papers)
  2. Zhongwei Wan (39 papers)
  3. Arvin Hekmati (7 papers)
  4. Mingyu Zong (5 papers)
  5. Samiul Alam (15 papers)
  6. Mi Zhang (85 papers)
  7. Bhaskar Krishnamachari (107 papers)
Citations (13)