DADM2D-SFL: Decentralised Aggregation framework based on DBSCAN Malicious Model Detection for Secure Federated Learning

Federated Learning (FL) is susceptible to adversarial attacks, such as Label Flipping (LF) and Backdoor, where malicious clients manipulate the updates of their local model to reduce the global model’s performance. Traditional FL relies on a centralised aggregator, which must be trusted, creating a single point of failure. This centralization not only increases computational cost but also introduces scalability challenges. To address these issues, we propose a Blockchain (BC) based FL framework that decentralises the aggregation process and incorporates an enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to identify and remove malicious updates without ignoring minor groups. Our approach eliminates the need for a centralised aggregator by leveraging BC’s smart contracts to aggregate the global model. Simultaneously, Enhanced DBSCAN identifies malicious updates in the parameter space, effectively mitigating adversarial influence while preserving privacy. We evaluate both of our framework and the traditional FL under LF red and Backdoor attacks, experimental results demonstrate that our approach outperforms the traditional FL according to multiple metrics, including accuracy, loss, precision, recall, and F1-score. These findings emphasise the effectiveness of our BC-based decentralised aggregation combined with enhanced DBSCAN technique in improving the robustness and security of FL systems.