A multilevel Monte Carlo framework debiases biased gradient compressors, preserving SGD convergence guarantees while reducing communication cost, with adaptive variance-minimizing level selection.
The proof follows very similarly to the one for the convex case
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Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
A multilevel Monte Carlo framework debiases biased gradient compressors, preserving SGD convergence guarantees while reducing communication cost, with adaptive variance-minimizing level selection.