FedCASKD: A Client-Aware Federated Distillation Framework for Robust Learning Under Heterogeneous Edge Environments

Federated Learning (FL) enables privacy-preserving model training in edge and IoT environments. However, in adversarial settings, FL suffers from two key challenges: robustness degradation due to data heterogeneity and poisoning attacks, and runtime instability on resource-constrained devices. Existing work mainly focuses on robustness while overlooking system-level stability. To address this, we propose FedCASKD, a robustness- and stability-aware FL framework. It employs a score-based soft aggregation mechanism to suppress unreliable client updates without requiring a trusted dataset, and introduces a selection-aware bidirectional knowledge distillation protocol to mitigate model drift under Non-IID data. The novelty lies in integrating aggregation and distillation into a unified feedback framework that enhances robustness and stability. Experiments on AGNews and SogouNews show that FedCASKD outperforms baselines under label-flipping attacks and heterogeneous settings. Memory and Out-of-Memory (OOM) tests further demonstrate its superior runtime stability in edge environments.