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Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G.
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T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A.-R. Sadeghi, “DÏot: A federated self-learning anomaly detection system for iot,” in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) , 2019, pp. 756–767
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W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y.-C. Liang, Q. Yang, D. T. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials , vol. 22, pp. 2031–2063, 2020
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T. Li, A. K. Sahu, A. S. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, pp. 50–60, 2020
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L. Liu, J. Zhang, S. Song, and K. B. Letaief, “Client-edge-cloud hierarchical federated learning,” in ICC 2020 - 2020 IEEE International Conference on Communications (ICC) , 2020, pp. 1–6
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X. Zeng, M. Yan, and M. Zhang, “Mercury: Efficient on-device distributed dnn training via stochastic importance sampling,” in The 19th ACM Conference on Embedded Networked Sensor Systems (SenSys’21) , 2021
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M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” IEEE Transactions on Wireless Communications , vol. 20, no. 1, pp. 269–283, 2021
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J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. G. Rabbat, M. Malekesmaeili, and D. Huba, “Federated learning with buffered asynchronous aggregation,” Federated Learning for User Privacy and Data Confidentiality Workshop At ICML , 2021
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T. Zhang, C. He, T. Ma, L. Gao, M. Ma, and S. Avestimehr, “Federated learning for internet of things,” in Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems , ser. SenSys ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 413–419. [Online]. Available: https://doi.org/10.1145/3485730.3493444
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2021
Cited alongside, same era.
A. Li, J. Sun, X. Zeng, M. Zhang, H. Li, and Y. Chen, “FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking,” in ACM Conference on Embedded Networked Sensor Systems (SenSys) , 2021
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2021
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A. Imteaj, U. Thakker, S. Wang, J. Li, and M. H. Amini, “A survey on federated learning for resource-constrained iot devices,” IEEE Internet of Things Journal , vol. 9, pp. 1–24, 2022
2022
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