Figures Abstract 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. Citation: Cheng F, Deng Z, Alobaedy MM, Huang X (2026) Federated parameter-free DBSCAN clustering and its application in image recognition. PLoS One 21(8): e0355161. https://doi.org/10.1371/journal.pone.0355161 Editor: Agbotiname Lucky Imoize, University of Lagos Faculty of Engineering, NIGERIA Received: November 6, 2025; Accepted: June 29, 2026; Published: August 4, 2026 Copyright: © 2026 Cheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All data that support the findings of this study are publicly available in the GitHub repository at https://github.com/mlyizhang/Clustering-Datasets. The repository contains all benchmark datasets required to replicate the