SSF enables efficient federated learning under heterogeneous data by optimizing in a low-dimensional subspace with projected corrections and backfill updates, achieving a non-asymptotic convergence rate of order O~(1/T + 1/sqrt(NKT)).
A unified linear speedup analysis of federated averaging and nesterov fedavg.Journal of Artificial Intelligence Research, 76:1345– 1386
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Subspace Optimization for Efficient Federated Learning under Heterogeneous Data
SSF enables efficient federated learning under heterogeneous data by optimizing in a low-dimensional subspace with projected corrections and backfill updates, achieving a non-asymptotic convergence rate of order O~(1/T + 1/sqrt(NKT)).