{"intvolume":"       267","type":"conference","page":"53757-53790","citation":{"apa":"Scott, J. A., Lampert, C., &#38; Saulpic, D. (2025). Differentially private federated k-means clustering with server-side data. In <i>42nd International Conference on Machine Learning</i> (Vol. 267, pp. 53757–53790). Vancouver, Canada: ML Research Press.","ista":"Scott JA, Lampert C, Saulpic D. 2025. Differentially private federated k-means clustering with server-side data. 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 53757–53790.","short":"J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 53757–53790.","ieee":"J. A. Scott, C. Lampert, and D. Saulpic, “Differentially private federated k-means clustering with server-side data,” in <i>42nd International Conference on Machine Learning</i>, Vancouver, Canada, 2025, vol. 267, pp. 53757–53790.","ama":"Scott JA, Lampert C, Saulpic D. Differentially private federated k-means clustering with server-side data. In: <i>42nd International Conference on Machine Learning</i>. Vol 267. ML Research Press; 2025:53757-53790.","mla":"Scott, Jonathan A., et al. “Differentially Private Federated K-Means Clustering with Server-Side Data.” <i>42nd International Conference on Machine Learning</i>, vol. 267, ML Research Press, 2025, pp. 53757–90.","chicago":"Scott, Jonathan A, Christoph Lampert, and David Saulpic. “Differentially Private Federated K-Means Clustering with Server-Side Data.” In <i>42nd International Conference on Machine Learning</i>, 267:53757–90. ML Research Press, 2025."},"file":[{"creator":"dernst","content_type":"application/pdf","date_created":"2025-12-16T12:38:29Z","date_updated":"2025-12-16T12:38:29Z","access_level":"open_access","relation":"main_file","checksum":"815b32b463023ca21e569c2158745c15","file_name":"2025_ICML_Scott.pdf","file_id":"20829","success":1,"file_size":746612}],"article_processing_charge":"No","publication_status":"published","oa_version":"Published Version","quality_controlled":"1","abstract":[{"text":"Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are increasingly being produced and stored in a distributed way, e.g. on edge devices, and privacy concerns prevent it from being transferred to a central server. To address this challenge, we present FedDP-KMeans, a new algorithm for \r\n-means clustering that is fully-federated as well as differentially private. Our approach leverages (potentially small and out-of-distribution) server-side data to overcome the primary challenge of differentially private clustering methods: the need for a good initialization. Combining our initialization with a simple federated DP-Lloyds algorithm we obtain an algorithm that achieves excellent results on synthetic and real-world benchmark tasks. We also provide a theoretical analysis of our method that provides bounds on the convergence speed and cluster identification success.","lang":"eng"}],"has_accepted_license":"1","alternative_title":["PMLR"],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"conference":{"end_date":"2025-07-19","name":"ICML: International Conference on Machine Learning","start_date":"2025-07-13","location":"Vancouver, Canada"},"external_id":{"arxiv":["2506.05408"]},"date_published":"2025-05-01T00:00:00Z","publication_identifier":{"eissn":["2640-3498"]},"date_created":"2025-12-14T23:02:05Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"acknowledgement":"This research was funded in part by the Austrian Science Fund (FWF) [10.55776/COE12] and supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp).\r\n","volume":267,"oa":1,"OA_type":"gold","arxiv":1,"month":"05","title":"Differentially private federated k-means clustering with server-side data","year":"2025","scopus_import":"1","file_date_updated":"2025-12-16T12:38:29Z","publisher":"ML Research Press","acknowledged_ssus":[{"_id":"ScienComp"}],"_id":"20819","OA_place":"publisher","publication":"42nd International Conference on Machine Learning","ddc":["000"],"day":"01","author":[{"first_name":"Jonathan A","full_name":"Scott, Jonathan A","last_name":"Scott","id":"e499926b-f6e0-11ea-865d-9c63db0031e8"},{"last_name":"Lampert","orcid":"0000-0001-8622-7887","first_name":"Christoph","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"first_name":"David","full_name":"Saulpic, David","last_name":"Saulpic","id":"f8e48cf0-b0ff-11ed-b0e9-b4c35598f964"}],"corr_author":"1","date_updated":"2025-12-16T12:39:56Z","status":"public","department":[{"_id":"ChLa"},{"_id":"MoHe"}]}