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Jing Chen

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Book Open access Aug 2026

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.

Nan Yan, Yuqing Li, Xiong Wang et al. · 0 citations
Conference Open access 2026

Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing

iFLoRA is proposed, an improved Federated LoRA fine-tuning system for LLMs featuring pipelined error-mitigated model aggregation and adaptive matrix-wise parameter freezing and can improve time-to-target by 2.17-8.48 × than state-of-the-art methods.

Haoran Wang, Xiong Wang, Yuqing Li et al. · 1 citation