Federated learning (FL) has gained popularity in the field of machine learning, which allows multiple participants to collaboratively learn a highly-accurate global model without exposing their sensitive data. However, FL is susceptible to poisoning attacks, in which malicious clients manipulate local model parameters to corrupt the global model. Existing FL frameworks based on detecting malicious clients suffer from unreasonable assumptions (e.g., clean validation datasets) or fail to balance robustness and efficiency. To address these deficiencies, we propose FedDMC, which implements robust federated learning by efficiently and precisely detecting malicious clients. Specifically, FedDMC first applies principal component analysis to reduce the dimensionality of the model parameters, which retains the primary parameter feature and reduces the computational overhead for subsequent clustering. Then, a binary tree-based clustering method with noise is designed to eliminate the effect of noisy points in the clustering process, facilitating accurate and efficient malicious client detection. Finally, we design a self-ensemble detection correction module that utilizes historical results via exponential moving averages to improve the robustness of malicious client detection. Extensive experiments conducted on three benchmark datasets demonstrate that FedDMC outperforms state-of-the-art methods in terms of detection precision, global model accuracy, and computational complexity.

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients tamper their model parameters to deteriorate the global model. Existing methods for defending against poisoning attacks primarily rely on identifying malicious clients, but struggle to balance robustness and efficiency. To address these issues, we propose FedPTA, a Prior-based Tensor Approximation (PTA) method. The core idea of FedPTA is to detect malicious clients in federated learning by leveraging inherent priors. This method initially innovatively defines multi-round model parameters as a three-dimensional tensor and unfolds it along different dimensions. Subsequently, three inherent priors - the similarity among benign clients, the continuity of multi-round client model parameters and the sparsity of malicious parameters, are integrated into a convex optimization framework. Through the optimization process, the optimal solutions for the background tensor and anomaly tensor are solved. Ultimately, the anomaly tensor is used to highlight the element-level features of malicious parameters, effectively distinguishing malicious clients. Evaluative studies supported by theoretical significance demonstrate the effectiveness of FedPTA, outperforming current state-of-the-art methods in terms of detection accuracy and computational efficiency.