{"id":"https://openalex.org/W3197363474","doi":"https://doi.org/10.1109/iwqos52092.2021.9521274","title":"FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models","display_name":"FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models","publication_year":2021,"publication_date":"2021-06-25","ids":{"openalex":"https://openalex.org/W3197363474","doi":"https://doi.org/10.1109/iwqos52092.2021.9521274","mag":"3197363474"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos52092.2021.9521274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos52092.2021.9521274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)","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/A5104666790","display_name":"Gaoyang Liu","orcid":"https://orcid.org/0000-0003-2566-9360"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Gaoyang Liu","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101637757","display_name":"Xiaoqiang Ma","orcid":"https://orcid.org/0000-0001-9987-0329"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqiang Ma","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397644","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-6297-5722"},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Hubei University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Hubei University, Wuhan, China","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337659","display_name":"Chen Wang","orcid":"https://orcid.org/0000-0003-1963-4954"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Wang","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039311485","display_name":"Jiangchuan Liu","orcid":"https://orcid.org/0000-0001-6592-1984"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jiangchuan Liu","raw_affiliation_strings":["Simon Fraser University, British Columbia, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, British Columbia, Canada","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5104666790"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":11.1967,"has_fulltext":false,"cited_by_count":174,"citation_normalized_percentile":{"value":0.98727157,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9994000196456909,"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":0.9975000023841858,"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":0.7740604281425476},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.505179226398468},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4224432110786438},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.3708742558956146},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.320201575756073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7740604281425476},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.505179226398468},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4224432110786438},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3708742558956146},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.320201575756073}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos52092.2021.9521274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos52092.2021.9521274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1488996941","https://openalex.org/W2473418344","https://openalex.org/W2530417694","https://openalex.org/W2535690855","https://openalex.org/W2535838896","https://openalex.org/W2541884796","https://openalex.org/W2585666524","https://openalex.org/W2744999500","https://openalex.org/W2795435272","https://openalex.org/W2798559676","https://openalex.org/W2810065831","https://openalex.org/W2903389359","https://openalex.org/W2911752833","https://openalex.org/W2911978475","https://openalex.org/W2912213068","https://openalex.org/W2930926105","https://openalex.org/W2948030332","https://openalex.org/W2963378725","https://openalex.org/W2963456518","https://openalex.org/W2963809394","https://openalex.org/W2971124187","https://openalex.org/W2971641579","https://openalex.org/W2985940692","https://openalex.org/W2995164118","https://openalex.org/W3003926725","https://openalex.org/W3007299160","https://openalex.org/W3009148757","https://openalex.org/W3021654819","https://openalex.org/W3033686777","https://openalex.org/W3035488832","https://openalex.org/W3035556513","https://openalex.org/W3035644192","https://openalex.org/W3037024761","https://openalex.org/W3095155273","https://openalex.org/W3103245149","https://openalex.org/W3103802018","https://openalex.org/W3113458348","https://openalex.org/W3119766471","https://openalex.org/W3138758728","https://openalex.org/W3154155772","https://openalex.org/W4287869905","https://openalex.org/W4288283361","https://openalex.org/W4297687186","https://openalex.org/W4318619660","https://openalex.org/W6690601626","https://openalex.org/W6728757088","https://openalex.org/W6732776586","https://openalex.org/W6749557519","https://openalex.org/W6752600739","https://openalex.org/W6756699189","https://openalex.org/W6765443569","https://openalex.org/W6767586073","https://openalex.org/W6769833289","https://openalex.org/W6770880833","https://openalex.org/W6771533808","https://openalex.org/W6773506501","https://openalex.org/W6773937556","https://openalex.org/W6774222418","https://openalex.org/W6774480952","https://openalex.org/W6787633081","https://openalex.org/W6788092230"],"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/W3176937389","https://openalex.org/W4388282301"],"abstract_inverted_index":{"Federated":[0],"learning":[1,11],"(FL)":[2],"has":[3],"recently":[4],"emerged":[5],"as":[6,246],"a":[7,115,205,260],"promising":[8],"distributed":[9],"machine":[10],"(ML)":[12],"paradigm.":[13],"Practical":[14],"needs":[15],"of":[16,50,114,139,165,180,211,228,234],"the":[17,40,48,63,67,94,104,112,120,127,132,144,156,161,173,177,191,201,209,212,217,226,240],"\"right":[18],"to":[19,62,78,97,142,189,198,208],"be":[20],"forgotten\"":[21],"and":[22,71,256,262],"countering":[23],"data":[24,38,81,118],"poisoning":[25],"attacks":[26],"call":[27],"for":[28,58,130,148],"efficient":[29,80],"techniques":[30,46],"that":[31,109,168],"can":[32,110],"remove,":[33],"or":[34],"unlearn,":[35],"specific":[36],"training":[37,178],"from":[39,74,83,239],"trained":[41],"FL":[42,70,84,122,134,251],"model.":[43,135],"Existing":[44],"unlearning":[45,107],"in":[47,56,66,250,259],"context":[49],"ML,":[51],"however,":[52],"are":[53,195],"no":[54],"longer":[55],"effect":[57],"FL,":[59],"mainly":[60],"due":[61],"inherent":[64],"distinction":[65],"way":[68],"how":[69,77],"ML":[72],"learn":[73],"data.":[75],"Therefore,":[76],"enable":[79],"removal":[82],"models":[85],"remains":[86],"largely":[87],"under-explored.":[88],"In":[89],"this":[90,99],"paper,":[91],"we":[92],"take":[93],"first":[95,105],"step":[96,249],"fill":[98],"gap":[100],"by":[101,159],"presenting":[102],"FedEraser,":[103,229],"federated":[106,116,166],"method-ology":[108],"eliminate":[111],"influence":[113],"client\u2019s":[117],"on":[119,221],"global":[121],"model":[123,158,214,218],"while":[124,215],"significantly":[125],"reducing":[126],"time":[128],"used":[129,197],"constructing":[131],"unlearned":[133,149,157,202,213],"The":[136],"basic":[137],"idea":[138],"FedEraser":[140,154],"is":[141,186],"trade":[143],"central":[145,174],"server\u2019s":[146],"storage":[147],"model\u2019s":[150],"construction":[151],"time,":[152],"where":[153],"reconstructs":[155],"leveraging":[160],"historical":[162],"parameter":[163],"updates":[164],"clients":[167],"have":[169],"been":[170],"retained":[171,192],"at":[172],"server":[175],"during":[176],"process":[179],"FL.":[181],"A":[182],"novel":[183],"calibration":[184],"method":[185],"further":[187,196],"developed":[188],"calibrate":[190],"updates,":[193],"which":[194],"promptly":[199],"construct":[200],"model,":[203],"yielding":[204],"significant":[206],"speed-up":[207,233],"reconstruction":[210],"maintaining":[216],"efficacy.":[219],"Experiments":[220],"four":[222],"realistic":[223],"datasets":[224],"demonstrate":[225],"effectiveness":[227],"with":[230,237,254],"an":[231,247],"expected":[232],"4\u00d7":[235],"compared":[236],"retraining":[238],"scratch.":[241],"We":[242],"envision":[243],"our":[244],"work":[245],"early":[248],"towards":[252],"compliance":[253],"legal":[255],"ethical":[257],"criteria":[258],"fair":[261],"transparent":[263],"manner.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":84},{"year":2024,"cited_by_count":57},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}