{"id":"https://openalex.org/W7160300286","doi":"https://doi.org/10.48550/arxiv.2605.02247","title":"Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning","display_name":"Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160300286","doi":"https://doi.org/10.48550/arxiv.2605.02247"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02247","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02247","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":null,"license_id":null,"version":null,"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":"https://doi.org/10.48550/arxiv.2605.02247","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135360288","display_name":"Shihao Hou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Shihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101668253","display_name":"Chikai Shang","orcid":"https://orcid.org/0009-0008-2914-9147"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Chikai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135322008","display_name":"Zhiheng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhiheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135390365","display_name":"Jiacheng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jiacheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135306920","display_name":"Xinyi Shang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Xinyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016259963","display_name":"Junlong Gao","orcid":"https://orcid.org/0000-0002-8734-1021"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Junlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135299514","display_name":"Yiqun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yiqun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135400439","display_name":"Yang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"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":0.5174000263214111,"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":0.5174000263214111,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.09719999879598618,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.07729999721050262,"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/personalization","display_name":"Personalization","score":0.8307999968528748},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.8062999844551086},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.5116000175476074},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5048999786376953},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.482699990272522},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.42239999771118164},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4212999939918518},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.41110000014305115}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8307999968528748},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.8062999844551086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7796000242233276},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5116000175476074},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.482699990272522},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4212999939918518},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C142039133","wikidata":"https://www.wikidata.org/wiki/Q3620943","display_name":"Personalized learning","level":5,"score":0.3878999948501587},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35929998755455017},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30489999055862427},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02247","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02247","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.02247","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02247","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Personalized":[0],"federated":[1,51],"learning":[2,52],"(PFL)":[3],"with":[4],"foundation":[5,78],"models":[6,90,165],"has":[7],"emerged":[8],"as":[9,140],"a":[10,133],"promising":[11],"paradigm":[12],"enabling":[13,120],"clients":[14],"to":[15,17,70,88,131],"adapt":[16],"heterogeneous":[18],"data":[19,30],"distributions.":[20],"However,":[21],"real-world":[22],"scenarios":[23],"often":[24],"face":[25],"the":[26,71,116,144],"co-occurrence":[27],"of":[28,73],"non-IID":[29],"and":[31,53,107,137,163],"long-tailed":[32,49,168],"class":[33,75],"distributions,":[34],"presenting":[35],"unique":[36],"challenges":[37],"that":[38,55,151],"remain":[39],"underexplored":[40],"in":[41,77,115],"PFL.":[42],"In":[43],"this":[44,48,86],"paper,":[45],"we":[46,100,124],"investigate":[47],"personalized":[50,164],"observe":[54],"current":[56],"methods":[57],"suffer":[58],"from":[59],"two":[60],"limitations:":[61],"(i)":[62],"fine-tuning":[63],"degrades":[64],"performance":[65,159],"below":[66],"zero-shot":[67,129],"baselines":[68],"due":[69],"erosion":[72],"inherent":[74],"balance":[76],"models;":[79],"(ii)":[80],"conventional":[81],"personalization":[82,139],"techniques":[83],"further":[84],"transfer":[85],"bias":[87],"local":[89,126],"through":[91],"parameter":[92],"or":[93],"feature-level":[94],"fusion.":[95],"To":[96],"address":[97],"these":[98],"challenges,":[99],"propose":[101],"Federated":[102],"Learning":[103,109],"via":[104],"Gradient":[105],"Purification":[106],"Residual":[108],"(FedPuReL),":[110],"which":[111],"preserves":[112],"balanced":[113],"knowledge":[114],"global":[117,135,146,162],"model":[118,138],"while":[119],"unbiased":[121],"personalization.":[122],"Specifically,":[123],"purify":[125],"gradients":[127],"using":[128],"predictions":[130],"maintain":[132],"class-balanced":[134],"model,":[136],"residual":[141],"correction":[142],"atop":[143],"frozen":[145],"model.":[147],"Extensive":[148],"experiments":[149],"demonstrate":[150],"FedPuReL":[152],"consistently":[153],"outperforms":[154],"state-of-the-art":[155],"methods,":[156],"achieving":[157],"superior":[158],"on":[160],"both":[161],"across":[166],"diverse":[167],"scenarios.":[169],"The":[170],"code":[171],"is":[172],"available":[173],"at":[174],"https://github.com/shihaohou/FedPuReL.":[175]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}
