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cs.LG2025
FedTopo: Topology-Informed Representation Alignment in Federated Learning under Non-I.I.D. Conditions
Ke Hu, Liyao Xiang, Peng Tang +1
Current federated-learning models deteriorate under heterogeneous (non-I.I.D.) client data, as their feature representations diverge and pixel- or patch-level objectives fail to ca…
cs.LG2023
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global Insights
Ke Hu, WeiDong Qiu, Peng Tang
In the field of federated learning, addressing non-independent and identically distributed (non-i.i.d.) data remains a quintessential challenge for improving global model performan…