Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models

• URL: http://arxiv.org/abs/2508.10349v1
• Date: Thu, 14 Aug 2025 05:14:00 GMT
• Title: Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
• Authors: Tianjun Yuan, Jiaxiang Geng, Pengchao Han, Xianhao Chen, Bing Luo,
• Abstract summary: Collaborative learning can leverage local clients' datasets for fine-tuning, but limited client data and heterogeneous data distributions hinder effective collaboration.<n>We propose a flexible personalized federated learning paradigm that enables clients to engage in collaborative learning while maintaining personalized objectives.
• Score: 5.346917610983131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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