{"id":"https://openalex.org/W4415428463","doi":"https://doi.org/10.3233/faia251020","title":"Out of Distribution Detection and Adaptive Interpolation for Personalized Federated Learning","display_name":"Out of Distribution Detection and Adaptive Interpolation for Personalized Federated Learning","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415428463","doi":"https://doi.org/10.3233/faia251020"},"language":null,"primary_location":{"id":"doi:10.3233/faia251020","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251020","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251020","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030488321","display_name":"Jacques Bures","orcid":null},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Jose Miguel Bur\u00e9s","raw_affiliation_strings":["CiTIUS. Universidade de Santiago de Compostela"],"affiliations":[{"raw_affiliation_string":"CiTIUS. Universidade de Santiago de Compostela","institution_ids":["https://openalex.org/I200284239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019268465","display_name":"R. Carabias Mart\u00ednez","orcid":null},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Roi Martinez","raw_affiliation_strings":["CiTIUS. Universidade de Santiago de Compostela"],"affiliations":[{"raw_affiliation_string":"CiTIUS. Universidade de Santiago de Compostela","institution_ids":["https://openalex.org/I200284239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103520424","display_name":"A. Gil N\u00fa\u00f1ez","orcid":null},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Alfonso Nu\u00f1ez","raw_affiliation_strings":["CiTIUS. Universidade de Santiago de Compostela"],"affiliations":[{"raw_affiliation_string":"CiTIUS. Universidade de Santiago de Compostela","institution_ids":["https://openalex.org/I200284239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086413658","display_name":"Roberto Iglesias","orcid":"https://orcid.org/0000-0002-6279-5190"},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Roberto Iglesias","raw_affiliation_strings":["CiTIUS. Universidade de Santiago de Compostela"],"affiliations":[{"raw_affiliation_string":"CiTIUS. Universidade de Santiago de Compostela","institution_ids":["https://openalex.org/I200284239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056902042","display_name":"Xos\u00e9 R. Fdez-Vidal","orcid":"https://orcid.org/0000-0001-9388-7461"},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Xos\u00e9 Ram\u00f3n Fernandez","raw_affiliation_strings":["CiTIUS. Universidade de Santiago de Compostela"],"affiliations":[{"raw_affiliation_string":"CiTIUS. Universidade de Santiago de Compostela","institution_ids":["https://openalex.org/I200284239"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100645872","display_name":"Javier Garc\u00eda","orcid":"https://orcid.org/0000-0002-5638-5240"},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Francisco Javier Garc\u00eda","raw_affiliation_strings":["CiTIUS. Universidade de Santiago de Compostela"],"affiliations":[{"raw_affiliation_string":"CiTIUS. Universidade de Santiago de Compostela","institution_ids":["https://openalex.org/I200284239"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5030488321"],"corresponding_institution_ids":["https://openalex.org/I200284239"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.50665452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5898000001907349},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5703999996185303},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.53329998254776},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.48570001125335693},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.45170000195503235},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4487000107765198},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.3880000114440918}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7997000217437744},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5898000001907349},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5703999996185303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5379999876022339},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5259000062942505},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4487000107765198},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39980000257492065},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.3880000114440918},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26159998774528503}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251020","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251020","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251020","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251020","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"enables":[3],"decentralized":[4],"training":[5],"across":[6],"multiple":[7],"clients":[8,130],"without":[9,164,173],"exposing":[10],"local":[11,108],"data.":[12],"However,":[13],"standard":[14],"FL":[15,159],"algorithms":[16],"struggle":[17],"under":[18,144],"heterogeneous":[19,149],"data":[20,109],"distributions,":[21],"often":[22],"leading":[23],"to":[24,29,54,59,110],"global":[25,84,136,166],"models":[26,33],"that":[27,34,152],"fail":[28],"generalize":[30],"and":[31,86,147,156],"personalized":[32,79,158],"ignore":[35],"collaborative":[36],"benefits.":[37],"Existing":[38],"approaches":[39],"based":[40,94,122],"on":[41,95,107,123,140],"model":[42,58],"interpolation,":[43],"such":[44],"as":[45],"APFL,":[46],"offer":[47],"a":[48,70,78,82,103,118],"promising":[49],"direction":[50],"but":[51],"lack":[52],"mechanisms":[53],"adaptively":[55],"select":[56],"which":[57,74],"use":[60],"at":[61],"inference":[62,93],"time.":[63],"In":[64],"this":[65],"work,":[66],"we":[67],"propose":[68],"FLProtector,":[69],"dual-model":[71],"framework":[72],"in":[73],"each":[75],"client":[76],"learns":[77],"increment":[80],"over":[81],"shared":[83],"model,":[85],"dynamically":[87],"selects":[88],"between":[89],"the":[90,127,135,141],"two":[91],"during":[92],"novelty":[96],"detection.":[97],"This":[98],"decision":[99],"is":[100],"guided":[101],"by":[102],"client-specific":[104],"autoencoder":[105],"trained":[106],"identify":[111],"out-of-distribution":[112],"inputs.":[113],"Our":[114],"method":[115],"also":[116],"incorporates":[117],"robust":[119],"aggregation":[120],"strategy":[121],"gradient":[124],"consistency,":[125],"reducing":[126],"impact":[128],"of":[129],"whose":[131],"updates":[132],"deviate":[133],"from":[134],"optimization":[137],"path.":[138],"Experiments":[139],"Digit-five":[142],"benchmark":[143],"both":[145],"fully":[146],"partially":[148],"scenarios":[150],"demonstrate":[151],"FLProtector":[153],"outperforms":[154],"classical":[155],"state-of-the-art":[157],"baselines,":[160],"offering":[161],"superior":[162],"personalization":[163],"compromising":[165],"generalization.":[167],"Notably,":[168],"it":[169],"achieves":[170],"strong":[171],"results":[172],"requiring":[174],"sensitive":[175],"hyperparameter":[176],"tuning.":[177]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-24T00:00:00"}
