Yuqing Li

Efficient and Differentially Private Federated LLM Fine-Tuning on Heterogeneous Clients

i-FedLoRA provides privacy guarantees, improves model accuracy by up to 3.8%, and expedites training by 1.37-2.23×, and facilitates heterogeneous LoRA aggregation that selectively prioritizes high-confidence knowledge to filter DP-induced noise, thereby achieving robust knowledge transfer.