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However, most state-of-the-art clustering approaches require clients to possess labeled datasets and perform substantial local computation, limiting their applicability in real-world settings. To address these limitations, we introduce\n                    <jats:sc>CoLEDS<\/jats:sc>\n                    , a method for profiling unlabeled client datasets with minimal computational overhead.\n                    <jats:sc>CoLEDS<\/jats:sc>\n                    trains a model using a contrastive learning objective defined across multiple clients and optimized in a distributed fashion through joint client\u2013server coordination. The resulting model embeds key properties of client datasets into low-dimensional vectors that are shared with the server for clustering. Extensive empirical evaluation shows that these profiles accurately capture latent dataset characteristics. By clustering clients based on these representations,\n                    <jats:sc>CoLEDS<\/jats:sc>\n                    yields federatively trained models that are better aligned with individual data distributions and enables appropriate model assignment even for clients that do not participate in federated training.\n                  <\/jats:p>","DOI":"10.1007\/s10618-026-01240-9","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T10:10:39Z","timestamp":1783678239000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Label-free dataset profiling for federated client clustering"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4142-931X","authenticated-orcid":false,"given":"Boris","family":"Radovi\u010d","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5051-4283","authenticated-orcid":false,"given":"Marco","family":"Canini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9009-0024","authenticated-orcid":false,"given":"Veljko","family":"Pejovi\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"1240_CR1","unstructured":"Acar DAE, Zhao Y, Navarro RM, Mattina M, Whatmough PN, Saligrama V (2021) Federated learning based on dynamic regularization. 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