Hui Tian

Decentralized federated networks can survive Byzantine adversaries without enforcing consensus: predicting and clipping peer deviations against a client's own historical trajectory guarantees robust personalized convergence.

AEGIS slashes token recovery rates to nearly zero while preserving model performance, tackling all three critical channels of information leakage in federated learning.