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Local SGD: Unified Theory and New Efficient Methods
[article]
2020
arXiv
pre-print
We present a unified framework for analyzing local SGD methods in the convex and strongly convex regimes for distributed/federated training of supervised machine learning models. We recover several known methods as a special case of our general framework, including Local-SGD/FedAvg, SCAFFOLD, and several variants of SGD not originally designed for federated learning. Our framework covers both the identical and heterogeneous data settings, supports both random and deterministic number of local
arXiv:2011.02828v1
fatcat:regduy5gpfbh5lh2lebp4xirua