{"id":"https://openalex.org/W3195765557","doi":"https://doi.org/10.1145/3447548.3467281","title":"Federated Adversarial Debiasing for Fair and Transferable Representations","display_name":"Federated Adversarial Debiasing for Fair and Transferable Representations","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3195765557","doi":"https://doi.org/10.1145/3447548.3467281","mag":"3195765557","pmid":"https://pubmed.ncbi.nlm.nih.gov/35571559"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467281","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467281","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9105979","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071064449","display_name":"Junyuan Hong","orcid":"https://orcid.org/0000-0002-5718-5187"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junyuan Hong","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079428801","display_name":"Zhuangdi Zhu","orcid":"https://orcid.org/0000-0002-7418-731X"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuangdi Zhu","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013851801","display_name":"Shuyang Yu","orcid":"https://orcid.org/0000-0003-1889-0163"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuyang Yu","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048522863","display_name":"Zhangyang Wang","orcid":"https://orcid.org/0000-0002-2050-5693"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhangyang Wang","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021979900","display_name":"Hiroko H. Dodge","orcid":"https://orcid.org/0000-0001-7290-8307"},"institutions":[{"id":"https://openalex.org/I165690674","display_name":"Oregon Health & Science University","ror":"https://ror.org/009avj582","country_code":"US","type":"education","lineage":["https://openalex.org/I165690674"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hiroko H. Dodge","raw_affiliation_strings":["Oregon Health &amp; Science University, Portland, OR, USA"],"affiliations":[{"raw_affiliation_string":"Oregon Health &amp; Science University, Portland, OR, USA","institution_ids":["https://openalex.org/I165690674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047215778","display_name":"Jiayu Zhou","orcid":"https://orcid.org/0000-0003-4336-6777"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Zhou","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5071064449"],"corresponding_institution_ids":["https://openalex.org/I87216513"],"apc_list":null,"apc_paid":null,"fwci":4.2152,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.95073849,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"2021","issue":null,"first_page":"617","last_page":"627"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9775999784469604,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9345999956130981,"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/debiasing","display_name":"Debiasing","score":0.9754546880722046},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8436328172683716},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8406949043273926},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5948339700698853},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.502100944519043},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4445631206035614},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.41883689165115356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.374217689037323},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3297353982925415}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9754546880722046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8436328172683716},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8406949043273926},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5948339700698853},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.502100944519043},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4445631206035614},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.41883689165115356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.374217689037323},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3297353982925415},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3447548.3467281","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467281","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},{"id":"pmid:35571559","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35571559","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:9105979","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9105979","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"KDD","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:9105979","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9105979","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"KDD","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6600000262260437,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G5145065809","display_name":null,"funder_award_id":"IIS-1749940, EPCN-2053272","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5346312292","display_name":null,"funder_award_id":"R01AG051628, R01AG056102, P30AG066518, P30AG024978, RF1AG072449","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1722318740","https://openalex.org/W1983349802","https://openalex.org/W2100960835","https://openalex.org/W2104094955","https://openalex.org/W2161574907","https://openalex.org/W2162651021","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2400943792","https://openalex.org/W2541884796","https://openalex.org/W2557449848","https://openalex.org/W2591882872","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2767079719","https://openalex.org/W2771063166","https://openalex.org/W2962864421","https://openalex.org/W2962897020","https://openalex.org/W2963116854","https://openalex.org/W2967880504","https://openalex.org/W2980216952","https://openalex.org/W3035453001","https://openalex.org/W3035668299","https://openalex.org/W3099314130","https://openalex.org/W3111381038","https://openalex.org/W3192693288","https://openalex.org/W6600838504","https://openalex.org/W6676033746"],"related_works":["https://openalex.org/W4362554880","https://openalex.org/W4281684980","https://openalex.org/W4386875279","https://openalex.org/W2171721708","https://openalex.org/W4221165959","https://openalex.org/W4225810998","https://openalex.org/W4280601492","https://openalex.org/W4286912360","https://openalex.org/W3204423820","https://openalex.org/W4320017490"],"abstract_inverted_index":{"Federated":[0,83],"learning":[1,5,25,32,47,53],"is":[2,8,48],"a":[3,80,114,146],"distributed":[4],"framework":[6,163],"that":[7,118],"communication":[9],"efficient":[10],"and":[11,31,39,75,97,144,176,181],"provides":[12],"protection":[13],"over":[14],"participating":[15],"users'":[16,29,91],"raw":[17],"training":[18],"data.":[19],"One":[20],"outstanding":[21],"challenge":[22],"of":[23,160,172],"federate":[24],"comes":[26],"from":[27,33,104],"the":[28,65,100,105,123,128,131,153,158,161,169],"heterogeneity,":[30],"such":[34],"data":[35],"may":[36,140],"yield":[37],"biased":[38],"unfair":[40],"models":[41,183],"for":[42,54,95],"minority":[43],"groups.":[44],"While":[45],"adversarial":[46,106],"commonly":[49],"used":[50],"in":[51,142],"centralized":[52,132],"mitigating":[55],"bias,":[56],"there":[57],"are":[58,184],"significant":[59],"barriers":[60,74],"when":[61,108,137],"extending":[62],"it":[63],"to":[64,102,151],"federated":[66],"framework.":[67],"In":[68],"this":[69],"work,":[70],"we":[71,156],"study":[72],"these":[73],"address":[76,152],"them":[77],"by":[78,130],"proposing":[79],"novel":[81],"approach":[82],"Adversarial":[84],"DEbiasing":[85],"(FADE).":[86],"FADE":[87,120],"does":[88],"not":[89],"require":[90],"sensitive":[92],"group":[93],"information":[94],"debiasing":[96],"offers":[98],"users":[99],"freedom":[101],"opt-out":[103],"component":[107],"privacy":[109],"or":[110],"computational":[111],"costs":[112],"become":[113],"concern.":[115],"We":[116,134],"show":[117],"ideally,":[119],"can":[121],"attain":[122],"same":[124],"global":[125],"optimality":[126],"as":[127],"one":[129],"algorithm.":[133],"then":[135],"analyze":[136],"its":[138],"convergence":[139],"fail":[141],"practice":[143],"propose":[145],"simple":[147],"yet":[148],"effective":[149],"method":[150],"problem.":[154],"Finally,":[155],"demonstrate":[157],"effectiveness":[159],"proposed":[162],"through":[164],"extensive":[165],"empirical":[166],"studies,":[167],"including":[168],"problem":[170],"settings":[171],"unsupervised":[173],"domain":[174],"adaptation":[175],"fair":[177],"learning.":[178],"Our":[179],"codes":[180],"pre-trained":[182],"available":[185],"at:":[186],"https://github.com/illidanlab/FADE.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
