Anh-Kiet Vo

FedIPSA: Personalized Federated Learning via Importance-Based Parameter Masking and Selective Alignment

Federated learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it well-suited for privacy-sensitive Internet of Things (IoT) environments. However, under heterogeneous [nonindependent and identically distributed (IID)] data distributions, classical FL often suffers...