2 papers
cs.DC2025
FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs
Jiaxiang Geng, Tianjun Yuan, Pengchao Han +3
To fine-tune large language models (LLMs) over private data, federated learning (FL) has emerged as a promising paradigm. However, the prohibitive memory and communication demands…
cs.LG2024
SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
Xinyang Liu, Pengchao Han, Xuan Li +1
Decentralized federated learning (DFL) realizes cooperative model training among connected clients without relying on a central server, thereby mitigating communication bottlenecks…