Toward Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence
Federated learning (FL) enables decentralized model training without sharing raw data but remains vulnerable to adversarial attacks and often suffers from performance degradation under non-IID data distributions. By analyzing vanilla knowledge distillation (VKD) and adversarial knowledge distillation (AKD), we observe...