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• W4361985268 abstract "In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network, <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$N$ </tex-math></inline-formula> users collaboratively train a global model assisted by <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$M$ </tex-math></inline-formula> intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relay-assisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network." @default.
• W4361985268 created "2023-04-05" @default.
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• W4361985268 date "2023-06-01" @default.
• W4361985268 modified "2023-10-15" @default.
• W4361985268 title "Relay-Assisted Federated Edge Learning: Performance Analysis and System Optimization" @default.
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• W4361985268 doi "https://doi.org/10.1109/tcomm.2023.3263566" @default.
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