SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data

• URL: http://arxiv.org/abs/2511.09828v2
• Date: Sun, 16 Nov 2025 21:33:02 GMT
• Title: SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data
• Authors: Mingkun Yang, Ran Zhu, Qing Wang, Jie Yang,
• Abstract summary: Split Federated Learning uses rich computing resources at a central server to train model partitions.<n>Data heterogeneity across silos presents a major challenge undermining the convergence speed and accuracy of the global model.<n>This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence.
• Score: 11.41105795202393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence arising from data heterogeneity by synchronizing the momentum buffers across server-side optimizers. To control gradient divergence over the training process, we design a staleness-aware alignment mechanism that imposes constraints on gradient updates of the server-side submodel at each optimization step. Extensive validations on multiple real-world datasets show that SMoFi consistently improves global model accuracy (up to 7.1%) and convergence speed (up to 10.25$\times$). Furthermore, SMoFi has a greater impact with more clients involved and deeper learning models, making it particularly suitable for model training in resource-constrained contexts.

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