The role of communication time in the convergence of federated edge learning

Yipeng Zhou, Yao Fu, Zhenxiao Luo, Miao Hu, Di Wu*, Quan Z. Sheng, Shui Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Federated Edge Learning (FEL) enables a massive number of edge devices (e.g. smart phones) to train machine learning models collaboratively. Due to the inherent unreliability of participating edge devices and the unpredictable network conditions, the final model accuracy is largely determined by the communication time between edge devices and the parameter server (PS). However, there exists very limited work to quantify the influence of the communication time in FEL and show how to minimize its negative impacts from a theoretic perspective. In this paper, we are among the first to develop a formal model of the communication time in FEL and its influence on the final model accuracy. In our work, the set of edge devices involved in each global iteration is defined as the ECP (Engaged Client Pool). We model the communication time cost as a function with respect to the response time distribution of individual devices and the ECP size. By incorporating communication time cost into the convergence rate analysis, we propose the ECPA and H-ECPA algorithms to automatically adjust the size of the ECP so as to maximize the model accuracy in both homogeneous and heterogeneous networks. We also analyze how the tail shape of response time affects the convergence rate, and prove that the heavy tail shape can significantly lower the model accuracy. Finally, we conduct extensive experiments with real datasets, and the results confirm the correctness of our analysis and demonstrate the superiority of our proposed algorithms.

Original languageEnglish
Pages (from-to)3241-3254
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Issue number3
Early online date20 Jan 2022
Publication statusPublished - Mar 2022


  • Analytical models
  • communication time
  • Computational modeling
  • Convergence
  • Costs
  • Federated edge learning
  • model accuracy
  • response time
  • Shape
  • Time factors
  • Training


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