  Federated learning (FL) is a privacy-preserving distributed framework for
collaborative model training on devices in edge networks. However, challenges
arise due to vulnerability to adversarial examples (AEs) and the
non-independent and identically distributed (non-IID) nature of data
distribution among devices, hindering the deployment of adversarially robust
and accurate learning models at the edge. While adversarial training (AT) is
commonly acknowledged as an effective defense strategy against adversarial
attacks in centralized training, we shed light on the adverse effects of
directly applying AT in FL that can severely compromise accuracy, especially in
non-IID challenges. Given this limitation, this paper proposes FatCC, which
incorporates local logit \underline{C}alibration and global feature
\underline{C}ontrast into the vanilla federated adversarial training
(\underline{FAT}) process from both logit and feature perspectives. This
approach can effectively enhance the federated system's robust accuracy (RA)
and clean accuracy (CA). First, we propose logit calibration, where the logits
are calibrated during local adversarial updates, thereby improving adversarial
robustness. Second, FatCC introduces feature contrast, which involves a global
alignment term that aligns each local representation with unbiased global
features, thus further enhancing robustness and accuracy in federated
adversarial environments. Extensive experiments across multiple datasets
demonstrate that FatCC achieves comparable or superior performance gains in
both CA and RA compared to other baselines.
