{"ID":2882156,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10349","arxiv_id":"2508.10349","title":"Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models","abstract":"Fine-tuning foundation models is critical for superior performance on personalized downstream tasks, compared to using pre-trained models. Collaborative learning can leverage local clients' datasets for fine-tuning, but limited client data and heterogeneous data distributions hinder effective collaboration. To address the challenge, we propose a flexible personalized federated learning paradigm that enables clients to engage in collaborative learning while maintaining personalized objectives. Given the limited and heterogeneous computational resources available on clients, we introduce \\textbf{flexible personalized split federated learning (FlexP-SFL)}. Based on split learning, FlexP-SFL allows each client to train a portion of the model locally while offloading the rest to a server, according to resource constraints. Additionally, we propose an alignment strategy to improve personalized model performance on global data. Experimental results show that FlexP-SFL outperforms baseline models in personalized fine-tuning efficiency and final accuracy.","short_abstract":"Fine-tuning foundation models is critical for superior performance on personalized downstream tasks, compared to using pre-trained models. Collaborative learning can leverage local clients' datasets for fine-tuning, but limited client data and heterogeneous data distributions hinder effective collaboration. To address...","url_abs":"https://arxiv.org/abs/2508.10349","url_pdf":"https://arxiv.org/pdf/2508.10349v1","authors":"[\"Tianjun Yuan\",\"Jiaxiang Geng\",\"Pengchao Han\",\"Xianhao Chen\",\"Bing Luo\"]","published":"2025-08-14T05:14:00Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.LG\"]","methods":"[]","has_code":false}