作者位次 #4/6ACL 2026Poster· Natural Language Processing
Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge
Yebo Wu · Jingguang Li · Chunlin Tian · KaHou Tam · Zhijiang Guo · Li Li
摘要
Federated fine-tuning enables privacy-preserving LLM adaptation but faces a critical bottleneck: the disparity between LLMs’ high memory demands and edge devices’ limited capacity. To break the memory barrier, we propose Chain Federated Fine-tuning (ChainFed), an innovative paradigm that forgoes...