FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but statistical heterogeneity makes a single global adapter insufficient for personalized prediction. Existing personalized methods typically use the same low-rank structure fo...
Meng-Jun Yi, Huai-An Gu, Yi-Hao Ai et al.
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