Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning

Abstract:Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as Local SGD, Minibatch SGD, and their accelerated variants, they still exhibit communication-round complexity that scales with the total number of samples $N$. In this paper, we introduce Local MixVR, a distributed framework that integrates local updates with variance-reduction techniques to mitigate local noise. We show that Local MixVR is the first distributed method to eliminate the dependence of communication complexity on $N$, achieving a complexity that scales only with the number of workers $M$. In common regimes where $M<O\left(N^{1/4}\right)$, Local MixVR outperforms the state-of-the-art Minibatch Accelerated SGD baseline, bridging a long-standing gap in distributed optimization and establishing a new paradigm for communication-efficient training.

Machine Learning (cs.LG)

arXiv:2606.01128 [cs.LG]

(or arXiv:2606.01128v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.01128 Focus to learn more arXiv-issued DOI via DataCite

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