X. Zhou, Y. Deng, H. Xia, S. Wu and M. Bennis, "Time-Triggered Federated Learning Over Wireless Networks," in IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 11066-11079, Dec. 2022, doi: 10.1109/TWC.2022.3189601
Time-triggered federated learning over wireless networks
|Author:||Zhou, Xiaokang1; Deng, Yansha2; Xia, Huiyun1;|
1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2Department of Engineering, King’s College London, London WC2R 2LS, U.K.
3Centre for Wireless Communications (CWC), University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 4.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301183456
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-18
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.
IEEE transactions on wireless communications
|Pages:||11066 - 11079|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Natural Science Foundation of China under Grant 61671173, Grant 62171163, and
Grant 61831002; and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/W004348/1. The work of Xiaokang Zhou was supported by the China Scholarship Council, and was performed during his visit at King’s College London.
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