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, Yu-Qing Li, Xiong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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.· Proceedings of the 32nd ACM...· 0 citations
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