  Federated learning (FL) is a privacy-preserving distributed management
framework based on collaborative model training of distributed devices in edge
networks. However, recent studies have shown that FL is vulnerable to
adversarial examples (AEs), leading to a significant drop in its performance.
Meanwhile, the non-independent and identically distributed (non-IID) challenge
of data distribution between edge devices can further degrade the performance
of models. Consequently, both AEs and non-IID pose challenges to deploying
robust learning models at the edge. In this work, we adopt the adversarial
training (AT) framework to improve the robustness of FL models against
adversarial example (AE) attacks, which can be termed as federated adversarial
training (FAT). Moreover, we address the non-IID challenge by implementing a
simple yet effective logits calibration strategy under the FAT framework, which
can enhance the robustness of models when subjected to adversarial attacks.
Specifically, we employ a direct strategy to adjust the logits output by
assigning higher weights to classes with small samples during training. This
approach effectively tackles the class imbalance in the training data, with the
goal of mitigating biases between local and global models. Experimental results
on three dataset benchmarks, MNIST, Fashion-MNIST, and CIFAR-10 show that our
strategy achieves competitive results in natural and robust accuracy compared
to several baselines.
