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)).
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag”uera y Arcas
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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)).