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cs.LG2026
SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums
Igor Sokolov, Laurent Condat, Peter Richtárik
Empirical risk minimization on massive datasets naturally exhibits a nested double finite-sum structure, where total samples are logically or physically partitioned into …
cs.LG2024
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
Georg Meinhardt, Kai Yi, Laurent Condat +1
In Federated Learning (FL), both client resource constraints and communication costs pose major problems for training large models. In the centralized setting, sparse training addr…