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math.OC2026
Bridging the Gap Between Homogeneous and Heterogeneous Asynchronous Optimization Is Surprisingly Difficult
Alexander Tyurin
Modern large-scale machine learning tasks often require multiple workers, devices, CPUs, or GPUs to compute stochastic gradients in parallel and asynchronously to train model weigh…
math.OC2025
Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
Alexander Tyurin
We consider centralized distributed optimization in the classical federated learning setup, where workers jointly find an -stationary point of an -smooth, -d…