CFLIT: Coexisting Federated Learning and Information Transfer

  • Zehong LIN
  • , Hang LIU*
  • , Ying Jun Angela ZHANG
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

12 Citations (Scopus)

Abstract

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

Original languageEnglish
Pages (from-to)8436-8453
Number of pages18
JournalIEEE Transactions on Wireless Communications
Volume22
Issue number11
Early online date4 Apr 2023
DOIs
Publication statusPublished - Nov 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.

Funding

This work was supported in part by the General Research Fund, under Project 14201920, Project 14202421, and Project 14214122, and in part by the Area of Excellence Scheme, under Project AoE/E-601/22-R, from the Research Grants Council of Hong Kong.

Keywords

  • coexistence
  • Edge intelligence
  • federated learning (FL)
  • multiple access
  • orthogonal frequency-division multiplexing (OFDM)
  • over-the-air computation

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