{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:19Z","timestamp":1761176179616,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Federated Learning (FL) enables decentralized training across multiple clients without exposing local data. However, standard FL algorithms struggle under heterogeneous data distributions, often leading to global models that fail to generalize and personalized models that ignore collaborative benefits. Existing approaches based on model interpolation, such as APFL, offer a promising direction but lack mechanisms to adaptively select which model to use at inference time. In this work, we propose FLProtector, a dual-model framework in which each client learns a personalized increment over a shared global model, and dynamically selects between the two during inference based on novelty detection. This decision is guided by a client-specific autoencoder trained on local data to identify out-of-distribution inputs. Our method also incorporates a robust aggregation strategy based on gradient consistency, reducing the impact of clients whose updates deviate from the global optimization path. Experiments on the Digit-five benchmark under both fully and partially heterogeneous scenarios demonstrate that FLProtector outperforms classical and state-of-the-art personalized FL baselines, offering superior personalization without compromising global generalization. Notably, it achieves strong results without requiring sensitive hyperparameter tuning.<\/jats:p>","DOI":"10.3233\/faia251020","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:49:08Z","timestamp":1761126548000},"source":"Crossref","is-referenced-by-count":0,"title":["Out of Distribution Detection and Adaptive Interpolation for Personalized Federated Learning"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4718-8901","authenticated-orcid":false,"given":"Jose Miguel","family":"Bur\u00e9s","sequence":"first","affiliation":[{"name":"CiTIUS. Universidade de Santiago de Compostela"}]},{"given":"Roi","family":"Martinez","sequence":"additional","affiliation":[{"name":"CiTIUS. Universidade de Santiago de Compostela"}]},{"given":"Alfonso","family":"Nu\u00f1ez","sequence":"additional","affiliation":[{"name":"CiTIUS. Universidade de Santiago de Compostela"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6279-5190","authenticated-orcid":false,"given":"Roberto","family":"Iglesias","sequence":"additional","affiliation":[{"name":"CiTIUS. Universidade de Santiago de Compostela"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9388-7461","authenticated-orcid":false,"given":"Xos\u00e9 Ram\u00f3n","family":"Fernandez","sequence":"additional","affiliation":[{"name":"CiTIUS. Universidade de Santiago de Compostela"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5638-5240","authenticated-orcid":false,"given":"Francisco Javier","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"CiTIUS. Universidade de Santiago de Compostela"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:49:08Z","timestamp":1761126548000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251020","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}