{"id":"https://openalex.org/W4391621115","doi":"https://doi.org/10.1109/access.2024.3363884","title":"FedFit: Server Aggregation Through Linear Regression in Federated Learning","display_name":"FedFit: Server Aggregation Through Linear Regression in Federated Learning","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391621115","doi":"https://doi.org/10.1109/access.2024.3363884"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3363884","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3363884","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10424985.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10424985.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075709878","display_name":"Taiga Kashima","orcid":"https://orcid.org/0000-0002-5709-4752"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Taiga Kashima","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-5709-4752","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051635592","display_name":"Ikki Kishida","orcid":"https://orcid.org/0000-0001-9454-7418"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ikki Kishida","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035540000","display_name":"Ayako Amma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ayako Amma","raw_affiliation_strings":["Woven by Toyota Inc., Chuo-ku, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Woven by Toyota Inc., Chuo-ku, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050229964","display_name":"Hideki Nakayama","orcid":"https://orcid.org/0000-0001-8726-2780"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideki Nakayama","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8726-2780","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075709878"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9762,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77839792,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"22803","last_page":"22812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T10237","display_name":"Cryptography and Data Security","score":0.9997000098228455,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9965999722480774,"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/computer-science","display_name":"Computer science","score":0.8209651708602905},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5164287686347961},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4909849464893341},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29413458704948425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24102449417114258}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8209651708602905},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5164287686347961},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4909849464893341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29413458704948425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24102449417114258}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3363884","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3363884","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10424985.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e3e948b52f824b429cfe89fa28c8f3a8","is_oa":true,"landing_page_url":"https://doaj.org/article/e3e948b52f824b429cfe89fa28c8f3a8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 22803-22812 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3363884","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3363884","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10424985.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391621115.pdf","grobid_xml":"https://content.openalex.org/works/W4391621115.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W2164013269","https://openalex.org/W2250539671","https://openalex.org/W2490662969","https://openalex.org/W2535838896","https://openalex.org/W2750384547","https://openalex.org/W2886444620","https://openalex.org/W2896422817","https://openalex.org/W2900120080","https://openalex.org/W2972570881","https://openalex.org/W2990595670","https://openalex.org/W3016632787","https://openalex.org/W3021654819","https://openalex.org/W3118608800","https://openalex.org/W3196371845","https://openalex.org/W3205701848","https://openalex.org/W4213177592","https://openalex.org/W4213446860","https://openalex.org/W4224227775","https://openalex.org/W4295806247","https://openalex.org/W4309345943","https://openalex.org/W6631190155","https://openalex.org/W6638444622","https://openalex.org/W6684229031","https://openalex.org/W6685053522","https://openalex.org/W6728757088","https://openalex.org/W6732298257","https://openalex.org/W6743688258","https://openalex.org/W6748786018","https://openalex.org/W6752600739","https://openalex.org/W6754708698","https://openalex.org/W6755988804","https://openalex.org/W6756840679","https://openalex.org/W6758757267","https://openalex.org/W6759238902","https://openalex.org/W6765541894","https://openalex.org/W6767676916","https://openalex.org/W6768511045","https://openalex.org/W6773817997","https://openalex.org/W6773976177","https://openalex.org/W6779308105","https://openalex.org/W6784336702","https://openalex.org/W6787972765","https://openalex.org/W6789100154","https://openalex.org/W6790358555","https://openalex.org/W6803097442","https://openalex.org/W6803116340","https://openalex.org/W6847350444","https://openalex.org/W6851888872"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4378677776","https://openalex.org/W3176937389"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,13,30,34,51,56,81,127],"conceptually":[3],"novel":[4],"framework":[5,21,103],"for":[6,12],"Federated":[7],"Learning":[8],"(FL)":[9],"called":[10],"FedFit":[11,20],"flexible":[14],"solver":[15,84],"to":[16,28,36,62,91,97,101,115,173],"address":[17,116],"FL":[18,121],"problems.":[19],"consists":[22],"of":[23,41,120,134,138,145,180],"two":[24],"components:":[25],"model":[26,32,45,54,165,176,181],"compression":[27],"upload":[29,50,93],"local":[31,44,53],"from":[33,95,126,166],"client":[35],"the":[37,42,47,60,64,74,86,98,105,135,143,157,163,167,174,178],"server":[38,61,65,87,106,124],"and":[39,148,170],"reconstruction":[40],"compressed":[43,52],"in":[46,85],"server.":[48,99],"Clients":[49],"using":[55],"\u201ckey\u201d":[57],"shared":[58],"with":[59],"formulate":[63],"aggregation":[66,125],"as":[67],"<italic":[68],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[69],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">linear":[70],"regression</i>":[71],".":[72],"Therefore,":[73],"global":[75,164,175],"model\u2019s":[76],"parameters":[77],"are":[78],"updated":[79],"through":[80],"linear":[82,112],"regression":[83,113,147,150],"while":[88],"naturally":[89],"contributing":[90],"reducing":[92],"costs":[94],"clients":[96,169],"Thanks":[100],"our":[102,139],"design,":[104],"can":[107,155],"flexibly":[108],"utilize":[109],"various":[110],"established":[111],"techniques":[114],"some":[117],"open":[118],"problems":[119],"by":[122],"considering":[123],"different":[128],"perspective\u2014linear":[129],"regression.":[130],"As":[131],"an":[132],"example":[133],"broad":[136],"applicability":[137],"idea,":[140],"we":[141],"demonstrate":[142],"effectiveness":[144],"robust":[146],"LASSO":[149],"implemented":[151],"on":[152,162],"FedFit,":[153],"which":[154],"alleviate":[156],"vulnerability":[158],"issues":[159],"against":[160],"attacks":[161],"collapsed":[168],"introduce":[171],"sparsity":[172],"toward":[177],"reduction":[179],"size,":[182],"respectively.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
