A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning

Authors: Minghui Chen, Hrad Ghoukasian, Ruinan Jin, Zehua Wang, Sai Praneeth Karimireddy, Xiaoxiao Li

Publication: arXiv preprint, 2025

Overview

Local fine-tuning can distort features learned by a federated global model, reducing generalization under client heterogeneity. This work studies linear probing followed by full fine-tuning across seven datasets and six personalization variants. Its empirical and theoretical analyses identify conditions in which phased updates provide a better balance between local adaptation and generalization.