Modular Distributed Nonconvex Learning with Error Feedback

• URL: http://arxiv.org/abs/2503.14055v1
• Date: Tue, 18 Mar 2025 09:16:51 GMT
• Title: Modular Distributed Nonconvex Learning with Error Feedback
• Authors: Guido Carnevale, Nicola Bastianello,
• Abstract summary: We design a novel distributed learning algorithm using compressed communications.<n>In detail, we pursue a modular approach, ADMM and a gradient-based approach.
• Score: 1.3198143828338362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we design a novel distributed learning algorithm using stochastic compressed communications. In detail, we pursue a modular approach, merging ADMM and a gradient-based approach, benefiting from the robustness of the former and the computational efficiency of the latter. Additionally, we integrate a stochastic integral action (error feedback) enabling almost sure rejection of the compression error. We analyze the resulting method in nonconvex scenarios and guarantee almost sure asymptotic convergence to the set of stationary points of the problem. This result is obtained using system-theoretic tools based on stochastic timescale separation. We corroborate our findings with numerical simulations in nonconvex classification.

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