4 papers
A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Chunxu Zhang, Bo Li, Wenliang Wang +7
Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order…
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
Xiaojin Zhang, Mingcong Xu, Yiming Li +2
Federated learning (FL) offers a promising paradigm for collaborative model training while preserving data privacy. However, its susceptibility to gradient inversion attacks poses…
Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives
Jiaqi Shao, Tao Lin, Xiaojin Zhang +2
Federated Unlearning (FU) enables the removal of specific clients' data influence from trained models. However, in non-IID settings, removing clients creates critical side effects:…