Decentralized federated learning (DFL) offers enhanced resilience to client failures compared to its centralized counterpart, due to its ability to aggregate learning models from distributed clients without the need for centralized server coordination. However, the presence of malicious Byzantine clients can degrade the performance of DFL algorithms, or even cause the entire learning process to fail. To combat Byzantine clients in DFL, this paper proposes a straightforward yet efficient algorithm to detect malicious updates using a small dataset sampled from clients’ local dataset. The proposed Local Performance Checking (LPC) algorithm allows each client to calculate the performance metrics (e.g., classification accuracy) of received updates from their neighbors using a pre-sampled performance test dataset. Updates whose performance metrics deviate significantly from those of the local model’s are identified and filtered out. The effectiveness of the proposed algorithm is demonstrated through extensive simulations across a wide range of practical scenarios.