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

• URL: http://arxiv.org/abs/2410.23660v1
• Date: Thu, 31 Oct 2024 06:20:17 GMT
• Title: Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
• Authors: Minghui Chen, Meirui Jiang, Xin Zhang, Qi Dou, Zehua Wang, Xiaoxiao Li,
• Abstract summary: We propose an innovative model-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. We demonstrated its effectiveness and efficiency across diverse widely-used FL datasets.
• Score: 33.88701368538447
• License:
• 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 \href{https://github.com/ubc-tea/Local-Superior-Soups}{https://github.com/ubc-tea/Local-Superior-Soups}.

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