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The Internet of Federated Things (IoFT) represents a network ofinterconnected systems with federated learning as the backbone, facilitatingcollaborative knowledge acquisition while ensuring data privacy for individualsystems. The wide adoption of IoFT, however, is hindered by security concerns,particularly the susceptibility of federated learning networks to adversarialattacks. In this paper, we propose an effective non-parametric approach FedRR,which leverages the low-rank features of the transmitted parameter updatesgenerated by federated learning to address the adversarial attack problem.Besides, our proposed method is capable of accurately detecting adversarialclients and controlling the false alarm rate under the scenario with no attackoccurring. Experiments based on digit recognition using the MNIST datasetsvalidated the advantages of our approach.

Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things