On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI

TL;DR

This paper analyzes how imperfect channel knowledge affects federated learning convergence over wireless networks and proposes methods to reduce training time by considering channel statistics and client participation.

Contribution

It introduces a federated learning training process that accounts for imperfect CSI and optimizes convergence time by selectively involving clients based on channel conditions.

Findings

Reducing training time by ignoring clients with poor transmission rates.

Trade-off between number of participating clients and model accuracy.

Channel statistics can be used to improve convergence under imperfect CSI.

Abstract

Federated learning (FL) has recently emerged as an attractive decentralized solution for wireless networks to collaboratively train a shared model while keeping data localized. As a general approach, existing FL methods tend to assume perfect knowledge of the Channel State Information (CSI) during the training phase, which may not be easy to acquire in case of fast fading channels. Moreover, literature analyses either consider a fixed number of clients participating in the training of the federated model, or simply assume that all clients operate at the maximum achievable rate to transmit model data. In this paper, we fill these gaps by proposing a training process that takes channel statistics as a bias to minimize the convergence time under imperfect CSI. Numerical experiments demonstrate that it is possible to reduce the training time by neglecting model updates from clients that cannot sustain a minimum predefined transmission rate. We also examine the trade-off between number of clients involved in the training process and model accuracy as a function of different fading regimes.