3 papers
cs.AI2024
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging
Yichu Xu, Xin-Chun Li, Le Gan +1
Merging models becomes a fundamental procedure in some applications that consider model efficiency and robustness. The training randomness or Non-I.I.D. data poses a huge challenge…
cs.LG2024
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
Xin-Chun Li, Shaoming Song, Yinchuan Li +4
In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients wi…
cs.LG2023
MrTF: Model Refinery for Transductive Federated Learning
Xin-Chun Li, Yang Yang, De-Chuan Zhan
We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protecti…