[00972] Reducing Communication in Federated Learning with Variance Reduction Methods
Session Time & Room : 4E (Aug.24, 17:40-19:20) @D408
Type : Contributed Talk
Abstract : In Federated Learning $\text{(}$FL$\text{)}$, inter-client heterogeneity and partial participation of clients at each communication cause client sampling error. We control this client sampling error by developing a novel single-loop variance reduction algorithm. While sampling a small number of clients, the proposed FL algorithms require provably fewer or at least equivalent communication rounds compared to any existing method, for finding first and even second-order stationarypoints in the general nonconvex setting, and under the PL condition.
