Online Learning with Adversaries: A Differential-Inclusion Analysis
Swetha Ganesh,
Alexandre Reiffers-Masson,
Gugan Thoppe
Abstract:We introduce an observation-matrix-based framework for fully asynchronous online Federated Learning (FL) with adversaries. In this work, we demonstrate its effectiveness in estimating the mean of a random vector. Our main result is that the proposed algorithm almost surely converges to the desired mean µ. This makes ours the first asynchronous FL method to have an a.s. convergence guarantee in the presence of adversaries. We derive this convergence using a novel differential-inclusion-based two-timescale analy… Show more
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