Author

Meng-Jun Yi

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#artificial intelligence Preprint Sep 2026

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. · 0 citations

#artificial intelligence Preprint Sep 2026

CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning

Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental mismatches under heterogeneous client data: a structural aggregation mismatch caused by independently averaging LoRA factors, and a statistical collaboration mismatch ca...

Meng-Jun Yi, Lang-Xing Yang, Su-Han Guo et al. · 0 citations