3 papers
cs.LG2026
Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving
Xixi Tian, Di Wu, Xiang Liu +4
Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated lea…
cs.CV2026
FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
Huan Wang, Jun Shen, Haoran Li +6
Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spo…
cs.LG2025
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
Di Wu, Qian Li, Heng Yang +1
Federated Learning (FL) enables geographically distributed clients to collaboratively train machine learning models by sharing only their local models, ensuring data privacy. Howev…