Trading-off Accuracy and Communication Cost in Federated Learning

Abstract

Leveraging the training-by-pruning paradigm introduced by Zhou et al. and Isik et al. introduced a federated learning protocol that achieves a 34-fold reduction in communication cost. We achieve a compression improvements of orders of orders of magnitude over the state-of-the-art. The central idea of our framework is to encode the network weights w by a the vector of trainable parameters p, such that w = Q p where Q is a carefully-generate sparse random matrix (that remains fixed throughout training). In such framework, the previous work of Zhou et al. [NeurIPS'19] is retrieved when Q is diagonal and p has the same dimension of w. We instead show that p can effectively be chosen much smaller than w, while retaining the same accuracy at the price of a decrease of the sparsity of Q. Since server and clients only need to share p, such a trade-off leads to a substantial improvement in communication cost. Moreover, we provide theoretical insight into our framework and establish a novel link between training-by-sampling and random convex geometry.

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Reproductions