{"id":"https://openalex.org/W6891915334","doi":"https://doi.org/10.48550/arxiv.2508.10732","title":"APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares","display_name":"APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares","publication_year":2025,"publication_date":"2025-08-14","ids":{"openalex":"https://openalex.org/W6891915334","doi":"https://doi.org/10.48550/arxiv.2508.10732"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2508.10732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.10732","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.2508.10732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fan, Kejia","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fan, Kejia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tang, Jianheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Jianheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yang, Zhirui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhirui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Han, Feijiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Feijiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Jiaxu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiaxu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"He, Run","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Run","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Huang, Yajiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yajiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Anfeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Anfeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Song, Houbing Herbert","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Houbing Herbert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Yunhuai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yunhuai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Zhuang, Huiping","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuang, Huiping","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"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":true,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.6107000112533569,"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":0.6107000112533569,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.09749999642372131,"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":0.05400000140070915,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7795000076293945},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7541000247001648},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7419000267982483},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6033999919891357},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.39309999346733093},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.3917999863624573},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.3855000138282776},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.37959998846054077},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3154999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.805899977684021},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7795000076293945},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7541000247001648},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7419000267982483},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6033999919891357},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3716999888420105},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3666999936103821},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31459999084472656},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3012999892234802},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28220000863075256},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C142039133","wikidata":"https://www.wikidata.org/wiki/Q3620943","display_name":"Personalized learning","level":5,"score":0.2754000127315521},{"id":"https://openalex.org/C2776937656","wikidata":"https://www.wikidata.org/wiki/Q2229669","display_name":"Nesting (process)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25360000133514404},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25200000405311584},{"id":"https://openalex.org/C89057211","wikidata":"https://www.wikidata.org/wiki/Q432197","display_name":"Collective intelligence","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2508.10732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.10732","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.2508.10732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.10732","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Personalized":[0,54],"Federated":[1,55],"Learning":[2,56],"(PFL)":[3],"has":[4],"presented":[5],"a":[6,68,72,100,111],"significant":[7],"challenge":[8],"to":[9,13,25,43,79,88],"deliver":[10],"personalized":[11,139],"models":[12,87],"individual":[14,94,120],"clients":[15],"through":[16],"collaborative":[17],"training.":[18],"Existing":[19],"PFL":[20],"methods":[21],"are":[22,149],"often":[23],"vulnerable":[24],"non-IID":[26,46],"data,":[27],"which":[28],"severely":[29],"hinders":[30],"collective":[31,91],"generalization":[32,92,106],"and":[33,93,110],"then":[34],"compromises":[35],"the":[36,80,147,162],"subsequent":[37],"personalization":[38,117],"efforts.":[39],"In":[40,63],"this":[41,45],"paper,":[42],"address":[44],"issue":[47],"in":[48,176],"PFL,":[49],"we":[50,66,83],"propose":[51],"an":[52],"Analytic":[53],"(APFL)":[57],"approach":[58],"via":[59],"dual-stream":[60,85],"least":[61,174],"squares.":[62],"our":[64,97,126,165],"APFL,":[65],"use":[67],"foundation":[69],"model":[70,140],"as":[71],"frozen":[73],"backbone":[74],"for":[75,104,115],"feature":[76,81],"extraction.":[77],"Subsequent":[78],"extractor,":[82],"develop":[84],"analytic":[86],"achieve":[89],"both":[90],"personalization.":[95],"Specifically,":[96],"APFL":[98,127,166],"incorporates":[99],"shared":[101],"primary":[102],"stream":[103,114],"global":[105],"across":[107,151,157],"all":[108,152],"clients,":[109],"dedicated":[112],"refinement":[113],"local":[116],"of":[118,125,132,144,164,172],"each":[119,138],"client.":[121],"The":[122],"analytical":[123],"solutions":[124],"enable":[128],"its":[129],"ideal":[130],"property":[131],"heterogeneity":[133],"invariance,":[134],"theoretically":[135],"meaning":[136],"that":[137],"remains":[141],"identical":[142],"regardless":[143],"how":[145],"heterogeneous":[146],"data":[148],"distributed":[150],"other":[153],"clients.":[154],"Empirical":[155],"results":[156],"various":[158],"datasets":[159],"also":[160],"validate":[161],"superiority":[163],"over":[167],"state-of-the-art":[168],"baselines,":[169],"with":[170],"advantages":[171],"at":[173],"1.10%-15.45%":[175],"accuracy.":[177]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
