A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Chen, Mingzhe; Yang, Zhaohui; Saad, Walid; Yin, Changchuan; Poor, H. Vincent; Cui, Shuguang

doi:10.1109/twc.2020.3024629

articleIEEE Transactions on Wireless CommunicationsOct 1, 2020Closed access

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

MCMingzhe Chen ZYZhaohui Yang WSWalid Saad CYChangchuan Yin HVH. Vincent Poor

Princeton University · Shenzhen Research Institute of Big Data · +4 more institutions

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Abstract

In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build…

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Topics & keywords

Topics

Keywords

Computer science
Wireless
Wireless network
Resource allocation
Computer network
Base station
Telecommunications link
Wi-Fi array

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