In this paper, the problem of distributed optimization is studied via a network of agents. Each agent only has access to a noisy gradient of its own objective function, and can communicate with its neighbors via a network. To handle this problem, a distributed clipped stochastic gradient descent algorithm is proposed, and the high probability convergence of the algorithm is studied. Existing works on distributed algorithms involving stochastic gradients only consider the light-tailed noises. Different from them, we study the case with heavy-tailed settings. Under mild assumptions on the graph connectivity, we prove that the algorithm converges in high probability under a certain clipping operator. Finally, a simulation is provided to demonstrate the effectiveness of our theoretical results
@article{arxiv.2506.11647,
title = {High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise},
author = {Yuchen Yang and Kaihong Lu and Long Wang},
journal= {arXiv preprint arXiv:2506.11647},
year = {2025}
}