{"id":"https://openalex.org/W4286974632","doi":"https://doi.org/10.1145/3534678.3539254","title":"Connecting Low-Loss Subspace for Personalized Federated Learning","display_name":"Connecting Low-Loss Subspace for Personalized Federated Learning","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4286974632","doi":"https://doi.org/10.1145/3534678.3539254"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539254","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539254","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539254","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539254","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066015639","display_name":"Seok-Ju Hahn","orcid":null},"institutions":[{"id":"https://openalex.org/I48566637","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748","country_code":"KR","type":"education","lineage":["https://openalex.org/I48566637"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seok-Ju Hahn","raw_affiliation_strings":["Ulsan National Institute of Science and Technology & Kakao Enterprise, Ulsan, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Ulsan National Institute of Science and Technology & Kakao Enterprise, Ulsan, Republic of Korea","institution_ids":["https://openalex.org/I48566637"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101694362","display_name":"Minwoo Jeong","orcid":"https://orcid.org/0000-0002-5112-9926"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minwoo Jeong","raw_affiliation_strings":["Kakao Enterprise, Seongnam, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Kakao Enterprise, Seongnam, Republic of Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007044119","display_name":"Junghye Lee","orcid":"https://orcid.org/0000-0002-9736-4796"},"institutions":[{"id":"https://openalex.org/I48566637","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748","country_code":"KR","type":"education","lineage":["https://openalex.org/I48566637"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junghye Lee","raw_affiliation_strings":["Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea","institution_ids":["https://openalex.org/I48566637"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066015639"],"corresponding_institution_ids":["https://openalex.org/I48566637"],"apc_list":null,"apc_paid":null,"fwci":1.9822,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.88473768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"505","last_page":"515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9625999927520752,"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/computer-science","display_name":"Computer science","score":0.8519977927207947},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7756139039993286},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.713250458240509},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7019760608673096},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6005581021308899},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.598787248134613},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5738150477409363},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48289966583251953},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.43366968631744385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4268730580806732},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40513336658477783},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.33101457357406616},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.16577860713005066},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.08320355415344238}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8519977927207947},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7756139039993286},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.713250458240509},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7019760608673096},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6005581021308899},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.598787248134613},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5738150477409363},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48289966583251953},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43366968631744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4268730580806732},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40513336658477783},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33101457357406616},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.16577860713005066},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.08320355415344238},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3534678.3539254","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539254","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539254","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2109.07628","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.07628","pdf_url":"https://arxiv.org/pdf/2109.07628","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:scholarworks.unist.ac.kr:201301/61575","is_oa":false,"landing_page_url":"https://scholarworks.unist.ac.kr/handle/201301/61575","pdf_url":null,"source":{"id":"https://openalex.org/S4306401118","display_name":"Scholarworks@UNIST (Ulsan National Institute of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I48566637","host_organization_name":"Ulsan National Institute of Science and Technology","host_organization_lineage":["https://openalex.org/I48566637"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"CONFERENCE"}],"best_oa_location":{"id":"doi:10.1145/3534678.3539254","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539254","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539254","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G4510440486","display_name":null,"funder_award_id":"2020R1C1C1011063","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G679586511","display_name":null,"funder_award_id":"2020-0-01336","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G745469241","display_name":null,"funder_award_id":"2020-0-01336","funder_id":"https://openalex.org/F4320321348","funder_display_name":"Ulsan National Institute of Science and Technology"},{"id":"https://openalex.org/G7982706382","display_name":null,"funder_award_id":"2020-0-01336","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G865289537","display_name":null,"funder_award_id":"Korea Government (MSIT)","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321348","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320324891","display_name":"Iran Telecommunication Research Center","ror":"https://ror.org/01a3g2z22"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4286974632.pdf","grobid_xml":"https://content.openalex.org/works/W4286974632.grobid-xml"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1850240193","https://openalex.org/W1899249567","https://openalex.org/W2007339694","https://openalex.org/W2164411961","https://openalex.org/W2182396527","https://openalex.org/W2194775991","https://openalex.org/W2551176409","https://openalex.org/W2626967530","https://openalex.org/W2788838181","https://openalex.org/W2793333878","https://openalex.org/W2807006176","https://openalex.org/W2909680570","https://openalex.org/W2911495555","https://openalex.org/W2912168444","https://openalex.org/W2951213900","https://openalex.org/W2951696358","https://openalex.org/W2951850597","https://openalex.org/W2951970475","https://openalex.org/W2952133801","https://openalex.org/W2963173418","https://openalex.org/W2963238274","https://openalex.org/W2963735582","https://openalex.org/W2966294393","https://openalex.org/W2971130081","https://openalex.org/W2972570881","https://openalex.org/W2976335444","https://openalex.org/W2981206218","https://openalex.org/W2990789643","https://openalex.org/W2992525328","https://openalex.org/W2996074092","https://openalex.org/W3005776401","https://openalex.org/W3006017224","https://openalex.org/W3007548213","https://openalex.org/W3012968339","https://openalex.org/W3021332602","https://openalex.org/W3038022836","https://openalex.org/W3080934299","https://openalex.org/W3099314130","https://openalex.org/W3104631511","https://openalex.org/W3111396641","https://openalex.org/W3118608800","https://openalex.org/W3129362180","https://openalex.org/W3129603732","https://openalex.org/W3130025260","https://openalex.org/W3136963962","https://openalex.org/W3172205380","https://openalex.org/W3213815372","https://openalex.org/W4213446860","https://openalex.org/W4226071455","https://openalex.org/W4285762978","https://openalex.org/W4286421857","https://openalex.org/W4287323488","https://openalex.org/W4287906413","https://openalex.org/W4288095202","https://openalex.org/W4289147229","https://openalex.org/W4297775537","https://openalex.org/W4300427714","https://openalex.org/W4318619660","https://openalex.org/W4361192893"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W3125756894","https://openalex.org/W4303448918"],"abstract_inverted_index":{"Due":[0],"to":[1,71,85,90,185],"the":[2,21,91,96,122,148,151,154],"curse":[3],"of":[4,24,31,52,81,98,132,150],"statistical":[5],"heterogeneity":[6],"across":[7],"clients,":[8],"adopting":[9],"a":[10,34,50,58,62,99,137],"personalized":[11,47,125,138],"federated":[12,25,53,63,126,139,155],"learning":[13,127,140],"method":[14,38,128,141,175],"has":[15,44,113],"become":[16],"an":[17,144],"essential":[18],"choice":[19],"for":[20,129,160],"successful":[22],"deployment":[23],"learning-based":[26],"services.":[27,191],"Among":[28],"diverse":[29,103],"branches":[30],"personalization":[32,37,181],"techniques,":[33],"model":[35,48,60,123,156],"mixture-based":[36,124],"is":[39,68,83],"preferred":[40],"as":[41,49],"each":[42,80,162],"client":[43],"their":[45],"own":[46],"result":[51],"learning.":[54],"It":[55],"usually":[56],"requires":[57,76],"local":[59,78,152],"and":[61,88,153,183],"model,":[64],"but":[65],"this":[66,118],"approach":[67],"either":[69],"limited":[70],"partial":[72],"parameter":[73],"exchange":[74],"or":[75,108],"additional":[77],"updates,":[79],"which":[82],"helpless":[84],"novel":[86],"clients":[87],"burdensome":[89],"client's":[92],"computational":[93],"capacity.":[94],"As":[95],"existence":[97],"connected":[100],"subspace":[101],"containing":[102],"low-loss":[104],"solutions":[105],"between":[106,147],"two":[107],"more":[109],"independent":[110],"deep":[111],"networks":[112],"been":[114],"discovered,":[115],"we":[116,171],"combined":[117],"interesting":[119],"property":[120],"with":[121],"improved":[130],"performance":[131,182],"personalization.":[133],"We":[134],"proposed":[135],"SuPerFed,":[136],"that":[142,173],"induces":[143],"explicit":[145],"connection":[146],"optima":[149],"in":[157,179,189],"weight":[158],"space":[159],"boosting":[161],"other.":[163],"Through":[164],"extensive":[165],"experiments":[166],"on":[167],"several":[168],"benchmark":[169],"datasets,":[170],"demonstrated":[172],"our":[174],"achieves":[176],"consistent":[177],"gains":[178],"both":[180],"robustness":[184],"problematic":[186],"scenarios":[187],"possible":[188],"realistic":[190]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}