arXiv Machine Learning
Aug 18

Global Federated Learning Strategies for Building Efficient Personalized Models

arXiv:2608. 15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously.

By Seongyoon Kim
arXiv AI
2d ago

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

The paper introduces FedRoRA, a federated learning framework that combines Low‑Rank Adaptation (LoRA) with rank‑heterogeneous personalization. It separates model adaptation into shared global directions and client‑specific rank‑wise magnitudes, using SVD on the server to extract a global subspace and a personalized projection with top‑k selection for each client. Experiments on natural language understanding and generation tasks show that FedRoRA outperforms existing state‑of‑the‑art methods.

By Lei Wang, Jieming Bian, Letian Zhang, Jie Xu