Federated Learning has emerged to cope with raising concerns about privacy
breaches in using Machine or Deep Learning models. This new paradigm allows the
leverage of deep learning models in a distributed manner, enhancing privacy
preservation. However, the server's blindness to local datasets introduces its
vulnerability to model poisoning attacks and data heterogeneity, tampering with
the global model performance. Numerous works have proposed robust aggregation
algorithms and defensive mechanisms, but the approaches are orthogonal to
individual attacks or issues. FedCC, the proposed method, provides robust
aggregation by comparing the Centered Kernel Alignment of Penultimate Layers
Representations. The experiment results on FedCC demonstrate that it mitigates
untargeted and targeted model poisoning or backdoor attacks while also being
effective in non-Independently and Identically Distributed data environments.
By applying FedCC against untargeted attacks, global model accuracy is
recovered the most. Against targeted backdoor attacks, FedCC nullified attack
confidence while preserving the test accuracy. Most of the experiment results
outstand the baseline methods.

Federated Learning 新范式弥补了使用机器或深度学习模型时隐私泄漏的担忧。FedCC 通过比较 net 不同层的标准核对其表示，提供了强大的聚合。实验结果表明，它缓解了非定向和有目的的模型污染或后门攻击，并且在非独立同分布数据环境中也有效。