{"id":"https://openalex.org/W4386075249","doi":"https://doi.org/10.1109/isit54713.2023.10206590","title":"Federated Learning with Local Fairness Constraints","display_name":"Federated Learning with Local Fairness Constraints","publication_year":2023,"publication_date":"2023-06-25","ids":{"openalex":"https://openalex.org/W4386075249","doi":"https://doi.org/10.1109/isit54713.2023.10206590"},"language":"en","primary_location":{"id":"doi:10.1109/isit54713.2023.10206590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit54713.2023.10206590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065836136","display_name":"Yuchen Zeng","orcid":"https://orcid.org/0000-0002-2766-0055"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuchen Zeng","raw_affiliation_strings":["University of Wisconsin,Madison","University of Wisconsin, Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin,Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009606065","display_name":"Hongxu Chen","orcid":"https://orcid.org/0000-0001-7963-8813"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongxu Chen","raw_affiliation_strings":["University of Wisconsin,Madison","University of Wisconsin, Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin,Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101899300","display_name":"Kangwook Lee","orcid":"https://orcid.org/0000-0002-3360-9678"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kangwook Lee","raw_affiliation_strings":["University of Wisconsin,Madison","University of Wisconsin, Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin,Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065836136"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":1.032,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.80755195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1937","last_page":"1942"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9968000054359436,"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"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9968000054359436,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9801999926567078,"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":0.6991757750511169},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6257905960083008},{"id":"https://openalex.org/keywords/fairness-measure","display_name":"Fairness measure","score":0.6229737401008606},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5730135440826416},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5278408527374268},{"id":"https://openalex.org/keywords/max-min-fairness","display_name":"Max-min fairness","score":0.41837966442108154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3847091794013977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3675908148288727},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3442647457122803},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.13369101285934448},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10760736465454102},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09005579352378845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6991757750511169},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6257905960083008},{"id":"https://openalex.org/C11867375","wikidata":"https://www.wikidata.org/wiki/Q5430671","display_name":"Fairness measure","level":4,"score":0.6229737401008606},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5730135440826416},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5278408527374268},{"id":"https://openalex.org/C177972170","wikidata":"https://www.wikidata.org/wiki/Q17097315","display_name":"Max-min fairness","level":3,"score":0.41837966442108154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3847091794013977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3675908148288727},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3442647457122803},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.13369101285934448},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10760736465454102},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09005579352378845},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit54713.2023.10206590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit54713.2023.10206590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2100960835","https://openalex.org/W2524301210","https://openalex.org/W2530395818","https://openalex.org/W2535838896","https://openalex.org/W2732560823","https://openalex.org/W2768894107","https://openalex.org/W2790025105","https://openalex.org/W2917462349","https://openalex.org/W2949200088","https://openalex.org/W2955213239","https://openalex.org/W2963116854","https://openalex.org/W2970850777","https://openalex.org/W3108091790","https://openalex.org/W3198906045","https://openalex.org/W3199340467","https://openalex.org/W3201304585","https://openalex.org/W3213859843","https://openalex.org/W4286912360","https://openalex.org/W4286971788","https://openalex.org/W4288319633","https://openalex.org/W4289258088","https://openalex.org/W4297687186","https://openalex.org/W4318619660","https://openalex.org/W6727155660","https://openalex.org/W6728551298","https://openalex.org/W6728757088","https://openalex.org/W6740797850","https://openalex.org/W6746225964","https://openalex.org/W6748039686","https://openalex.org/W6759853437","https://openalex.org/W6763290930","https://openalex.org/W6764294493","https://openalex.org/W6765541894","https://openalex.org/W6767301763","https://openalex.org/W6787245701","https://openalex.org/W6802161216","https://openalex.org/W6802466298","https://openalex.org/W6802667569"],"related_works":["https://openalex.org/W2002544160","https://openalex.org/W2084765981","https://openalex.org/W1586551486","https://openalex.org/W2057526413","https://openalex.org/W3140700844","https://openalex.org/W2142058354","https://openalex.org/W2136050782","https://openalex.org/W2296143973","https://openalex.org/W2994602514","https://openalex.org/W2119004634"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,90],"study":[4],"training":[5,21],"a":[6],"classifier":[7],"satisfying":[8],"local":[9,19],"fairness":[10,48,63,83],"constraints":[11,84],"via":[12],"federated":[13,33,76],"learning.":[14],"We":[15],"compare":[16],"two":[17],"schemes:":[18],"fair":[20],"(LFT)":[22],"combined":[23,31],"with":[24,32,85],"the":[25,50,61,71],"ensemble":[26],"method":[27],"(LFT+Ensemble),":[28],"and":[29,55],"LFT":[30],"averaging":[34],"(LFT+FedAvg).":[35],"Our":[36],"theoretical":[37],"analysis":[38],"shows":[39],"that":[40,79,95],"(i)":[41],"LFT+FedAvg":[42,57],"outperforms":[43],"LFT+Ensemble":[44],"in":[45,99],"terms":[46],"of":[47,52,73],"at":[49],"cost":[51],"frequent":[53],"communication,":[54],"(ii)":[56],"may":[58],"not":[59],"reach":[60],"optimal":[62],"achievable":[64],"through":[65],"centralized":[66],"training.":[67],"The":[68],"findings":[69,98],"explain":[70],"success":[72],"recently":[74],"proposed":[75],"learning":[77],"algorithms":[78],"virtually":[80],"compute":[81],"global":[82],"additional":[86],"communication":[87],"rounds.":[88],"Moreover,":[89],"also":[91],"present":[92],"numerical":[93],"experiments":[94],"support":[96],"our":[97],"more":[100],"general":[101],"settings.":[102]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}