Weishan Zhang

FedEMoE: Towards Class-Aware Federated Learning via Evidential Mixture-of-Experts

Federated learning under non-IID data suffers from client drift. Recent work introduces Mixture-of-Experts (MoE) architectures to handle data heterogeneity by routing inputs to specialized experts, but two problems remain. Softmax routing produces overconfident expert assignments with no reliability measure. The parame...

OIAFed: All-in-One Federated Learning Framework

Risk-Aware Communication Link Pruning for Secure Multi-Agent Systems

FedUP: Uncertainty-Aware Personalized Federated Learning via Probabilistic Prototypes

FedUP is an uncertainty-aware personalized federated learning framework that models prototypes as probability distributions and aggregates prototype distributions through reliability-informed barycenters on the statistical manifold, suppressing unreliable contributions while preserving geometric structure.