{"id":"https://openalex.org/W7127887817","doi":"https://doi.org/10.48550/arxiv.2602.04536","title":"Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning","display_name":"Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning","publication_year":2026,"publication_date":"2026-02-04","ids":{"openalex":"https://openalex.org/W7127887817","doi":"https://doi.org/10.48550/arxiv.2602.04536"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.04536","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101702632","display_name":"Abdulrahman Alotaibi","orcid":"https://orcid.org/0000-0002-9096-2443"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alotaibi, Abdulrahman","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045662127","display_name":"Irene Tenison","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tenison, Irene","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125227818","display_name":"Miriam Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Miriam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125250344","display_name":"Isaac Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Isaac","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013709154","display_name":"Lalana Kagal","orcid":"https://orcid.org/0000-0001-8469-1993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kagal, Lalana","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101702632"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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":0.27059999108314514,"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":0.27059999108314514,"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"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.10869999974966049,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.09669999778270721,"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/adaptation","display_name":"Adaptation (eye)","score":0.6816999912261963},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6690999865531921},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6629999876022339},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.5645999908447266},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5156999826431274},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.43709999322891235},{"id":"https://openalex.org/keywords/web-service","display_name":"Web service","score":0.4083000123500824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8203999996185303},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6816999912261963},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6690999865531921},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6629999876022339},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.5645999908447266},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.448199987411499},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C35578498","wikidata":"https://www.wikidata.org/wiki/Q193424","display_name":"Web service","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C11392498","wikidata":"https://www.wikidata.org/wiki/Q11288","display_name":"Web server","level":3,"score":0.36980000138282776},{"id":"https://openalex.org/C118643609","wikidata":"https://www.wikidata.org/wiki/Q189210","display_name":"Web application","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3237999975681305},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26249998807907104},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25699999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.04536","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.04536","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.04536","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.04536","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"The":[0],"Web":[1,30],"is":[2,24,62],"naturally":[3],"heterogeneous":[4,87,206],"with":[5],"user":[6,40],"devices,":[7,41],"geographic":[8],"regions,":[9],"browsing":[10],"patterns,":[11],"and":[12,44,93,126,132,142,152,207],"contexts":[13],"all":[14],"leading":[15],"to":[16,48,138,182],"highly":[17],"diverse,":[18],"unique":[19],"datasets.":[20,194],"Federated":[21,79],"Learning":[22],"(FL)":[23],"an":[25,189],"important":[26],"paradigm":[27,76],"for":[28,204],"the":[29,105,120,124,136,158,166,198],"because":[31],"it":[32],"enables":[33],"privacy-preserving,":[34],"collaborative":[35],"machine":[36],"learning":[37],"across":[38,193],"diverse":[39],"web":[42,66,209],"services":[43],"clients":[45,169],"without":[46],"needing":[47],"centralize":[49],"sensitive":[50],"data.":[51],"However,":[52],"its":[53,184],"performance":[54],"degrades":[55],"severely":[56],"under":[57],"non-IID":[58],"client":[59],"distributions":[60],"that":[61,83,157],"prevalent":[63],"in":[64,86],"real-world":[65,205],"systems.":[67,210],"In":[68],"this":[69],"work,":[70],"we":[71,97],"propose":[72],"a":[73,110],"new":[74],"training":[75,99],"-":[77,82],"Iterative":[78],"Adaptation":[80],"(IFA)":[81],"enhances":[84],"generalization":[85,185],"federated":[88,180],"settings":[89],"through":[90],"generation-wise":[91],"forget":[92,131],"evolve":[94,133],"strategy.":[95],"Specifically,":[96],"divide":[98],"into":[100],"multiple":[101],"generations":[102],"and,":[103],"at":[104],"end":[106],"of":[107,112,123,191,200],"each,":[108],"select":[109],"fraction":[111],"model":[113,125,137],"parameters":[114],"(a)":[115],"randomly":[116],"or":[117],"(b)":[118],"from":[119],"later":[121],"layers":[122],"reinitialize":[127],"them.":[128],"This":[129,172,195],"iterative":[130],"schedule":[134],"allows":[135],"escape":[139],"local":[140],"minima":[141],"preserve":[143],"globally":[144],"relevant":[145],"representations.":[146],"Extensive":[147],"experiments":[148],"on":[149,177],"CIFAR-10,":[150],"MIT-Indoors,":[151],"Stanford":[153],"Dogs":[154],"datasets":[155],"show":[156],"proposed":[159],"approach":[160],"improves":[161],"global":[162],"accuracy,":[163],"especially":[164],"when":[165],"data":[167],"cross":[168],"are":[170],"Non-IID.":[171],"method":[173],"can":[174],"be":[175],"implemented":[176],"top":[178],"any":[179],"algorithm":[181],"improve":[183],"performance.":[186],"We":[187],"observe":[188],"average":[190],"21.5%improvement":[192],"work":[196],"advances":[197],"vision":[199],"scalable,":[201],"privacy-preserving":[202],"intelligence":[203],"distributed":[208]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-07T00:00:00"}