Participation Bias Correction for Byzantine-Robust Federated Learning Under Heterogeneous Client Availability
Federated learning enables distributed clients to collaboratively train a global model without sharing their private data. However, Byzantine robustness becomes more challenging under heterogeneous client availability, since Byzantine clients with higher availability can participate more frequently than honest clients...
Xiao Ma, Hong Shen, Hui Tian et al.
· Electronics · 0 citations