[2604.02558] Communication-Efficient Distributed Learning with Differential Privacy

Computer Science > Machine Learning arXiv:2604.02558 (cs) [Submitted on 2 Apr 2026] Title:Communication-Efficient Distributed Learning with Differential Privacy Authors:Xiaoxing Ren, Yuwen Ma, Nicola Bastianello, Karl H. Johansson, Thomas Parisini, Andreas A. Malikopoulos View a PDF of the paper titled Communication-Efficient Distributed Learning with Differential Privacy, by Xiaoxing Ren and 5 other authors View PDF HTML (experimental) Abstract:We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents' data. The first goal is achieved through a local training approach, which reduces communication frequency. The second goal is achieved by perturbing gradients during local training, specifically through gradient clipping and additive noise. We prove that the resulting algorithm converges to a stationary point of the problem within a bounded distance. Additionally, we provide theoretical privacy guarantees within a differential privacy framework that ensure agents' training data cannot be inferred from the trained model shared over the network. We show the algorithm's superior performance on a classification task under the same privacy budget, compared with state-of-the-art methods. Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2604.02558 [cs.LG]   (or arXiv:2604.02558v1 [cs.LG] fo...