On the Convergence and Stability of Distributed Sub-model Training

Yuyang Deng; Fuli Qiao; Mehrdad Mahdavi

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arXiv · 2511.06132

On the Convergence and Stability of Distributed Sub-model Training

Yuyang Deng·Fuli Qiao·Mehrdad Mahdavi

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Abstract

As learning models continue to grow in size, enabling on-device local training of these models has emerged as a critical challenge in federated learning. A popular solution is sub-model training, where the server only distributes randomly sampled sub-models to the edge clients, and clients only update these small models. However, those random sampling of sub-models may not give satisfying convergence performance. In this paper, observing the success of SGD with shuffling, we propose a distributed shuffled sub-model training, where the full model is partitioned into several sub-models in advance, and the server shuffles those sub-models, sends each of them to clients at each round, and by the end of local updating period, clients send back the updated sub-models, and server averages them. We establish the convergence rate of this algorithm. We also study the generalization of distributed sub-model training via stability analysis, and find that the sub-model training can improve the generalization via amplifying the stability of training process. The extensive experiments also validate our theoretical findings.

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BibTeX RIS

Yuyang Deng, Fuli Qiao, Mehrdad Mahdavi. 2025-11-08. On the Convergence and Stability of Distributed Sub-model Training. https://arxiv.org/abs/2511.06132

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