Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning

Abstract:Federated learning (FL) is a learning paradigm that enables collaborative training of models using decentralized data. Recently, the utilization of pre-trained weight initialization in FL has been demonstrated to effectively improve model performance. However, the evolving complexity of current pre-trained models, characterized by a substantial increase in parameters, markedly intensifies the challenges associated with communication rounds required for their adaptation to FL. To address these communication cost issues and increase the performance of pre-trained model adaptation in FL, we propose an innovative model interpolation-based local training technique called ``Local Superior Soups.'' Our method enhances local training across different clients, encouraging the exploration of a connected low-loss basin within a few communication rounds through regularized model interpolation. This approach acts as a catalyst for the seamless adaptation of pre-trained models in in FL. We demonstrated its effectiveness and efficiency across diverse widely-used FL datasets. Our code is available at this https URL.

Machine Learning (cs.LG)

arXiv:2410.23660 [cs.LG]

(or arXiv:2410.23660v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2410.23660 Focus to learn more arXiv-issued DOI via DataCite

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