Fast-Convergent Federated Learning

Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However, convergence generally requires a large number of communication rounds, which induces delay in model training and is costly in terms of network resources. In this paper, we propose a fast-convergent federated learning algorithm, called $\mathsf {FOLB}$ , which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed. We first theoretically characterize a lower bound on improvement that can be obtained in each round if devices are selected according to the expected improvement their local models will provide to the current global model. Then, we show that $\mathsf {FOLB}$ obtains this bound through uniform sampling by weighting device updates according to their gradient information. $\mathsf {FOLB}$ is able to handle both communication and computation heterogeneity of devices by adapting the aggregations according to estimates of device's capabilities of contributing to the updates. We evaluate $\mathsf {FOLB}$ in comparison with existing federated learning algorithms and experimentally show its improvement in trained model accuracy, convergence speed, and/or model stability across various machine learning tasks and datasets.

Original languageEnglish (US)
Article number9252927
Pages (from-to)201-218
Number of pages18
JournalIEEE Journal on Selected Areas in Communications
Volume39
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Federated learning
  • distributed optimization
  • fast convergence rate

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