AI RESEARCH
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
arXiv CS.LG
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ArXi:2606.01128v1 Announce Type: new 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