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)).
Beyond local sharp- ness: Communication-efficient global sharpness-aware minimization for federated learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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)).