Abstract
In distributed optimization, and even more in federated learning, communication is the main bottleneck. We introduce LoCoDL, a communication-efficient algorithm that leverages the two techniques of Local training, which reduces the communication frequency, and Compression with a large class of unbiased compressors that includes sparsification and quantization strategies. LoCoDL provably benefits from the two mechanisms and enjoys a doubly-accelerated communication complexity, with respect to the condition number of the functions and the model dimension, in the general heterogenous regime with strongly convex functions.
Brief Biography
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