{"id":"https://openalex.org/W4221154841","doi":"https://doi.org/10.1145/3531146.3533081","title":"Minimax Demographic Group Fairness in Federated Learning","display_name":"Minimax Demographic Group Fairness in Federated Learning","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W4221154841","doi":"https://doi.org/10.1145/3531146.3533081"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533081","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533081","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","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":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2201.08304","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048760244","display_name":"Afroditi Papadaki","orcid":null},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Afroditi Papadaki","raw_affiliation_strings":["University College London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University College London, United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079873012","display_name":"Natalia J. Martinez","orcid":"https://orcid.org/0000-0002-0663-9011"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Natalia Martinez","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051941774","display_name":"Mart\u00edn Bertr\u00e1n","orcid":"https://orcid.org/0000-0003-4086-1078"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Bertran","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025218580","display_name":"Guillermo Sapiro","orcid":"https://orcid.org/0000-0001-9190-6964"},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]},{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guillermo Sapiro","raw_affiliation_strings":["Duke University, USA and Apple Inc., USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA and Apple Inc., USA","institution_ids":["https://openalex.org/I4210153776","https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026028064","display_name":"Miguel Tr\u00e9faut Rodrigues","orcid":"https://orcid.org/0000-0003-3958-9919"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Miguel Rodrigues","raw_affiliation_strings":["University College London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University College London, United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5048760244"],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":null,"apc_paid":null,"fwci":0.3131,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.47864335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"142","last_page":"159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.46369999647140503,"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":0.46369999647140503,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.21879999339580536,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.12219999730587006,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/minimax","display_name":"Minimax","score":0.8246357440948486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7632801532745361},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6660845279693604},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.5927078127861023},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5253286361694336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38826122879981995},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34853363037109375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3296690285205841},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.225301593542099},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09565269947052002},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.0613667368888855}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.8246357440948486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7632801532745361},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6660845279693604},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.5927078127861023},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5253286361694336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38826122879981995},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34853363037109375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3296690285205841},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.225301593542099},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09565269947052002},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0613667368888855},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3531146.3533081","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533081","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","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":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2201.08304","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.08304","pdf_url":"https://arxiv.org/pdf/2201.08304","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:eprints.ucl.ac.uk.OAI2:10172091","is_oa":false,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10172091/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"   FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency     pp. 142-159.   (2022)      ","raw_type":"Article"},{"id":"doi:10.48550/arxiv.2201.08304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2201.08304","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-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2201.08304","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.08304","pdf_url":"https://arxiv.org/pdf/2201.08304","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"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1511597886","display_name":null,"funder_award_id":"NSF-1737744, NSF-1712867,DMS-2031849","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7452925987","display_name":null,"funder_award_id":"814643","funder_id":"https://openalex.org/F4320306164","funder_display_name":"Simons Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306164","display_name":"Simons Foundation","ror":"https://ror.org/01cmst727"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1978259121","https://openalex.org/W2153015072","https://openalex.org/W2527563090","https://openalex.org/W2963917042","https://openalex.org/W3033733989","https://openalex.org/W3109723249","https://openalex.org/W3138531978","https://openalex.org/W3193066552","https://openalex.org/W3197345550","https://openalex.org/W3208283650"],"related_works":["https://openalex.org/W1407330","https://openalex.org/W6296663","https://openalex.org/W1279312","https://openalex.org/W364583","https://openalex.org/W1316385","https://openalex.org/W361876","https://openalex.org/W102453","https://openalex.org/W173424","https://openalex.org/W13954494","https://openalex.org/W796041"],"abstract_inverted_index":{"Federated":[0],"learning":[1,29,63,97,117],"is":[2],"an":[3,78],"increasingly":[4],"popular":[5],"paradigm":[6],"that":[7,66,89,120],"enables":[8],"a":[9,40],"large":[10],"number":[11],"of":[12,42,73,95,111],"entities":[13,34],"to":[14,39],"collaboratively":[15],"learn":[16],"better":[17],"models.":[18],"In":[19],"this":[20],"work,":[21],"we":[22],"study":[23],"minimax":[24],"group":[25,56,112],"fairness":[26,57,64,113],"in":[27,109,114],"federated":[28,62,116],"scenarios":[30],"where":[31],"different":[32],"participating":[33],"may":[35],"only":[36],"have":[37],"access":[38],"subset":[41],"the":[43,47,86,92,102],"population":[44],"groups":[45],"during":[46],"training":[48],"phase.":[49],"We":[50,76,99],"formally":[51],"analyze":[52],"how":[53],"our":[54,121],"proposed":[55,87,103],"objective":[58],"differs":[59],"from":[60],"existing":[61],"criteria":[65],"impose":[67],"similar":[68],"performance":[69,93],"across":[70],"participants":[71],"instead":[72],"demographic":[74],"groups.":[75],"provide":[77],"optimization":[79],"algorithm":[80],"\u2013":[81,83],"FedMinMax":[82],"for":[84],"solving":[85],"problem":[88],"provably":[90],"enjoys":[91],"guarantees":[94],"centralized":[96],"algorithms.":[98],"experimentally":[100],"compare":[101],"approach":[104,122],"against":[105],"other":[106],"state-of-the-art":[107],"methods":[108],"terms":[110],"various":[115],"setups,":[118],"showing":[119],"exhibits":[123],"competitive":[124],"or":[125],"superior":[126],"performance.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}