2 papers
cs.CL2026
FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
Zhilin Liang, Yuxiang Wang, Zimu Zhou +3
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding generation in external knowledge to improve factuality and reduce hallucinations. Yet most d…
cs.LG2026
CAFEDistill: Learning Personalized and Dynamic Models through Federated Early-Exit Network Distillation
Boyi Liu, Zimu Zhou, Yongxin Tong
Personalized Federated Learning (PFL) enables collaboratively model training on decentralized, heterogeneous data while tailoring them to each client's unique distribution. However…