2 papers
math.OC2025
Tight Lower Bounds and Optimal Algorithms for Stochastic Nonconvex Optimization with Heavy-Tailed Noise
Adrien Fradin, Abdurakhmon Sadiev, Laurent Condat +1
We study stochastic nonconvex optimization under heavy-tailed noise. In this setting, the stochastic gradients only have bounded -th central moment (-BCM) for some $p \in (1,…
math.OC2025
Local SGD and Federated Averaging Through the Lens of Time Complexity
Adrien Fradin, Peter Richtárik, Alexander Tyurin
We revisit the classical Local SGD and Federated Averaging (FedAvg) methods for distributed optimization and federated learning. While prior work has primarily focused on iteration…