Federated parameter-free DBSCAN clustering and its application in image recognition

DBSCAN (A Density-Based Algorithm for Discovering Clusters in Spatial Databases with Noise) is a classic clustering algorithm. However, clustering distributed data with privacy protection in edge computing environments is a key challenge for DBSCAN. In this research, we combine federated clustering and DBSCAN and propose two secure federated parameter-free DBSCAN clustering methods, called FDBSCAN and FDBSCAN++. The process involves the following steps: (1) differential privacy is applied to the client data and adaptive DBSCAN is used at each client to identify core points; (2) the clients send the extracted core points to the server, where the server aggregates these to obtain the final global cluster centers (FDBSCAN and FDBSCAN++ use different methods in this step); (3) the final clusters are generated using these global centers. To verify the effectiveness of the proposed two algorithms, we use eight real datasets, including the large-scale image dataset MNIST. Compared with traditional and state-of-the-art (SOTA) improved DBSCAN and federated clustering algorithms, the proposed algorithms achieve better clustering accuracy. In addition, we also apply FDBSCAN++ to image clustering and segmentation tasks, which achieves satisfactory results.