FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning.

arXiv:2609.37033v1 Announce Type: new Abstract: 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 insuffic