Decentralized Federated Learning Over Imperfect Communication Channels

Weicai Li, Tiejun Lv, Wei Ni, Jingbo Zhao, Ekram Hossain, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Communications
DOIs
StateAccepted/In press - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Analytical models
  • Convergence
  • convergence analysis
  • Decentralized federated learning
  • imperfect communication channel
  • Network topology
  • Protocols
  • Topology
  • Training
  • Upper bound

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