2 papers
cs.CV2025
pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation
Tong Wang, Xingyue Zhao, Linghao Zhuang +5
Medical image segmentation is crucial for computer-aided diagnosis, yet privacy constraints hinder data sharing across institutions. Federated learning addresses this limitation, b…
cs.LG2025
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast
Yiting Zheng, Bohan Lin, Jinqian Chen +1
Most current federated learning frameworks are modeled as static processes, ignoring the dynamic characteristics of the learning system. Under the limited communication budget of t…