标题/作者	年份
Federated learning for internet of things: Recent advances, taxonomy, and open challenges LU Khan, W Saad, Z Han, E Hossain, CS Hong IEEE Communications Surveys & Tutorials 23 (3), 1759-1799	2021
Federated learning: A survey on enabling technologies, protocols, and applications M Aledhari, R Razzak, RM Parizi, F Saeed IEEE Access 8, 140699-140725	2020
Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ... arXiv preprint arXiv:2007.13518	2020
Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems OA Wahab, A Mourad, H Otrok, T Taleb IEEE Communications Surveys & Tutorials 23 (2), 1342-1397	2021
Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective J Xu, H Wang IEEE Transactions on Wireless Communications 20 (2), 1188-1200	2020
Communication-efficient edge AI: Algorithms and systems Y Shi, K Yang, T Jiang, J Zhang, KB Letaief IEEE Communications Surveys & Tutorials 22 (4), 2167-2191	2020
HFEL: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning S Luo, X Chen, Q Wu, Z Zhou, S Yu IEEE Transactions on Wireless Communications 19 (10), 6535-6548	2020
A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning C She, C Sun, Z Gu, Y Li, C Yang, HV Poor, B Vucetic Proceedings of the IEEE 109 (3), 204-246	2021
Federated learning review: Fundamentals, enabling technologies, and future applications S Banabilah, M Aloqaily, E Alsayed, N Malik, Y Jararweh Information processing & management 59 (6), 103061	2022
Holistic network virtualization and pervasive network intelligence for 6G X Shen, J Gao, W Wu, M Li, C Zhou, W Zhuang IEEE Communications Surveys & Tutorials 24 (1), 1-30	2021
An efficiency-boosting client selection scheme for federated learning with fairness guarantee T Huang, W Lin, W Wu, L He, K Li, AY Zomaya IEEE Transactions on Parallel and Distributed Systems 32 (7), 1552-1564	2020
From distributed machine learning to federated learning: A survey J Liu, J Huang, Y Zhou, X Li, S Ji, H Xiong, D Dou Knowledge and Information Systems 64 (4), 885-917	2022
Federated learning for the internet of things: Applications, challenges, and opportunities T Zhang, L Gao, C He, M Zhang, B Krishnamachari, AS Avestimehr IEEE Internet of Things Magazine 5 (1), 24-29	2022
A systematic literature review on federated machine learning: From a software engineering perspective SK Lo, Q Lu, C Wang, HY Paik, L Zhu ACM Computing Surveys (CSUR) 54 (5), 1-39	2021
Optimizing federated learning in distributed industrial IoT: A multi-agent approach W Zhang, D Yang, W Wu, H Peng, N Zhang, H Zhang, X Shen IEEE Journal on Selected Areas in Communications 39 (12), 3688-3703	2021
EdgeFed: Optimized federated learning based on edge computing Y Ye, S Li, F Liu, Y Tang, W Hu IEEE Access 8, 209191-209198	2020
Federated learning in edge computing: a systematic survey HG Abreha, M Hayajneh, MA Serhani Sensors 22 (2), 450	2022
FedCPF: An efficient-communication federated learning approach for vehicular edge computing in 6G communication networks S Liu, J Yu, X Deng, S Wan IEEE Transactions on Intelligent Transportation Systems 23 (2), 1616-1629	2021
Toward resource-efficient federated learning in mobile edge computing R Yu, P Li IEEE Network 35 (1), 148-155	2021
Deep learning for ultra-reliable and low-latency communications in 6G networks C She, R Dong, Z Gu, Z Hou, Y Li, W Hardjawana, C Yang, L Song, ... IEEE network 34 (5), 219-225	2020

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