Gradient-free federated learning methods with \(l_1\) and \(l_2\)-randomization for non-smooth convex stochastic optimization problems
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Publication:6053598
DOI10.1134/s0965542523090026arXiv2211.10783OpenAlexW4387620276MaRDI QIDQ6053598
D. M. Dvinskikh, A. V. Gasnikov, B. A. Alashqar, A. V. Lobanov
Publication date: 19 October 2023
Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2211.10783
Related Items (2)
Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact ⋮ Non-smooth setting of stochastic decentralized convex optimization problem over time-varying graphs
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