Adversarial Client Detection via Non-parametric Subspace Monitoring in
  the Internet of Federated Things
Xianjian Xie Xiaochen Xian Dan Li Andi Wang
The Internet of Federated Things (IoFT) represents a network of
interconnected systems with federated learning as the backbone, facilitating
collaborative knowledge acquisition while ensuring data privacy for individual
systems. The wide adoption of IoFT, however, is hindered by security concerns,
particularly the susceptibility of federated learning networks to adversarial
attacks. In this paper, we propose an effective non-parametric approach FedRR,
which leverages the low-rank features of the transmitted parameter updates
generated by federated learning to address the adversarial attack problem.
Besides, our proposed method is capable of accurately detecting adversarial
clients and controlling the false alarm rate under the scenario with no attack
occurring. Experiments based on digit recognition using the MNIST datasets
validated the advantages of our approach.