LAUSR

This article presents communication-efficient approaches to federated learning (FL) for resource-constrained devices with access to streaming data. In particular, we first propose a partial-sharing-based framework for online federated learning (PSO-Fed), wherein clients update local models from a stream of data and exchange tiny fractions of the model with the server, reducing the communication overhead. In contrast to classical FL approaches, the proposed strategy provides clients who are not part of a global iteration with the freedom to update local models whenever new data arrives. Furthermore, by devising a client-side innovation check, we also propose an event-triggered PSO-Fed (ETPSO-Fed) that further reduces the computational burden of clients while enhancing communication efficiency. We implement the above-mentioned frameworks in the context of kernel regression, where clients perform local learning employing random Fourier features (RFFs)-based kernel least mean squares. In addition, we examine the mean and mean-square convergence of the proposed PSO-Fed. Finally, we conduct experiments to determine the efficacy of the proposed frameworks. Our results show that PSO-Fed and ETPSO-Fed can compete with Online-Fed while requiring significantly less communication overhead. Simulations demonstrate an 80% reduction in PSO-Fed and an 84.5% reduction in ETPSO-Fed communication overhead compared to Online-Fed. Notably, the proposed PSO-Fed strategies show good resilience against model-poisoning attacks without involving additional mechanisms.