Federated learning with multichannel ALOHA

TL;DR

This paper explores federated learning in cellular systems, demonstrating that multichannel ALOHA improves performance over sequential polling and proposing an adaptive, distributed method to optimize user access probabilities for better model aggregation.

Contribution

It introduces a novel application of multichannel ALOHA in federated learning and develops a distributed optimization approach for user access probability adjustment.

Findings

Multichannel ALOHA outperforms sequential polling in federated learning scenarios.

Optimized access probabilities enhance federated learning aggregation.

Distributed adaptation of access probabilities is feasible and effective.

Abstract

In this paper, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets. We show that multichannel random access can provide a better performance than sequential polling when some users are unable to compute local updates (due to other tasks) or in dormant state. In addition, for better aggregation in federated learning, the access probabilities of users can be optimized for given local updates. To this end, we formulate an optimization problem and show that a distributed approach can be used within federated learning to adaptively decide the access probabilities.