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Communication-efficient federated learning

Chen, Mingzhe ; Shlezinger, Nir ; Poor, H Vincent ; Eldar, Yonina C ; Cui, Shuguang

Proceedings of the National Academy of Sciences - PNAS, 2021-04, Vol.118 (17), p.1 [Periódico revisado por pares]

United States: National Academy of Sciences

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  • Título:
    Communication-efficient federated learning
  • Autor: Chen, Mingzhe ; Shlezinger, Nir ; Poor, H Vincent ; Eldar, Yonina C ; Cui, Shuguang
  • Assuntos: Bandwidths ; Communication ; Communication networks ; Convergence ; Electronic devices ; Exchanging ; Federated learning ; Internet of Things ; Learning algorithms ; Machine learning ; Mathematical models ; Parameters ; Physical Sciences ; Resource allocation ; Training ; Wireless communications ; Wireless networks
  • É parte de: Proceedings of the National Academy of Sciences - PNAS, 2021-04, Vol.118 (17), p.1
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Contributed by H. Vincent Poor, March 2, 2021 (sent for review December 1, 2020; reviewed by Georgios B. Giannakis and Anit Kumar Sahu)
    Author contributions: M.C., N.S., H.V.P., Y.C.E., and S.C. designed research; M.C., N.S., and H.V.P. performed research; M.C. and H.V.P. analyzed data; and M.C., N.S., H.V.P., Y.C.E., and S.C. wrote the paper.
    Reviewers: G.B.G., University of Minnesota; and A.K.S., Amazon (United States).
  • Descrição: Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively, and thus the time it requires to jointly learn a reliable model depends not only on the number of training steps but also on the ML parameter transmission time per step. In practice, FL parameter transmissions are often carried out by a multitude of participating devices over resource-limited communication networks, for example, wireless networks with limited bandwidth and power. Therefore, the repeated FL parameter transmission from edge devices induces a notable delay, which can be larger than the ML model training time by orders of magnitude. Hence, communication delay constitutes a major bottleneck in FL. Here, a communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss. In this framework, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission. To further reduce the FL convergence time, a quantization method is proposed to reduce the volume of the model parameters exchanged among devices, and an efficient wireless resource allocation scheme is developed. Simulation results show that the proposed FL framework can improve the identification accuracy and convergence time by up to 3.6% and 87% compared to standard FL.
  • Editor: United States: National Academy of Sciences
  • Idioma: Inglês
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