3 papers
cs.LG2026
When the Server Steps In: Calibrated Updates for Fair Federated Learning
Tianrun Yu, Kaixiang Zhao, Cheng Zhang +4
Federated learning (FL) has emerged as a transformative distributed learning paradigm, enabling multiple clients to collaboratively train a global model under the coordination of a…
cs.LG2026
Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection
Zhiwei Ling, Hailiang Zhao, Chao Zhang +8
Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes while preserving data privacy, making it a cornerstone of intelligent serv…
cs.LG2025
Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data
Yipu Zhang, Chengshuo Zhang, Ziyu Zhou +5
Data privacy constraints pose significant challenges for large-scale neuroimaging analysis, especially in multi-site functional magnetic resonance imaging (fMRI) studies, where sit…