{"id":"https://openalex.org/W4414170674","doi":"https://doi.org/10.1109/iwqos65803.2025.11143467","title":"Eliminating Poor-Quality Data Impacts from Multiple Participants with Federated Unlearning","display_name":"Eliminating Poor-Quality Data Impacts from Multiple Participants with Federated Unlearning","publication_year":2025,"publication_date":"2025-07-02","ids":{"openalex":"https://openalex.org/W4414170674","doi":"https://doi.org/10.1109/iwqos65803.2025.11143467"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos65803.2025.11143467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos65803.2025.11143467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM 33rd 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/A5100399541","display_name":"Pengfei Wang","orcid":"https://orcid.org/0000-0001-8658-7102"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengfei Wang","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075612401","display_name":"Muhammad Ameen","orcid":"https://orcid.org/0009-0006-8216-6099"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muhammad Ameen","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057159244","display_name":"Mingshu Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingshu Zhao","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062643911","display_name":"Pai Liu","orcid":"https://orcid.org/0000-0003-2227-9762"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pai Liu","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100381999","display_name":"Qiang Zhang","orcid":"https://orcid.org/0000-0003-3776-9799"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China,116024","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100399541"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12530179,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9865000247955322,"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":0.965399980545044,"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/federated-learning","display_name":"Federated learning","score":0.6995999813079834},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5814999938011169},{"id":"https://openalex.org/keywords/neglect","display_name":"Neglect","score":0.3522000014781952},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3133000135421753},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.29789999127388},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.2759000062942505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577999830245972},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6995999813079834},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5814999938011169},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4302000105381012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4083999991416931},{"id":"https://openalex.org/C2776289891","wikidata":"https://www.wikidata.org/wiki/Q1931511","display_name":"Neglect","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.337799996137619},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2750999927520752},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2632000148296356},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.260699987411499}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos65803.2025.11143467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos65803.2025.11143467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM 33rd International Symposium on Quality of Service (IWQoS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2811577194","display_name":null,"funder_award_id":"62202080","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2750384547","https://openalex.org/W2807006176","https://openalex.org/W2963318081","https://openalex.org/W2963819344","https://openalex.org/W3092396074","https://openalex.org/W3118608800","https://openalex.org/W3159623990","https://openalex.org/W3196325542","https://openalex.org/W3197363474","https://openalex.org/W3209576002","https://openalex.org/W4226128336","https://openalex.org/W4283208007","https://openalex.org/W4285428850","https://openalex.org/W4289147229","https://openalex.org/W4312058280","https://openalex.org/W4312454804","https://openalex.org/W4321521256","https://openalex.org/W4389747890","https://openalex.org/W4390044356","https://openalex.org/W4391326488","https://openalex.org/W4391953554","https://openalex.org/W4392908036","https://openalex.org/W4393252682","https://openalex.org/W4400810322","https://openalex.org/W4402082682","https://openalex.org/W4402742360","https://openalex.org/W4402897278","https://openalex.org/W4402897322","https://openalex.org/W4412565294"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"unlearning":[1,5],"(FUL)":[2],"facilitates":[3],"targeted":[4],"of":[6,67,92,168,186,209],"data-specific":[7],"artifacts":[8],"from":[9,38,73,95,171],"trained":[10],"federated":[11],"learning":[12],"(FL)":[13],"models.":[14],"Existing":[15],"methodologies":[16],"primarily":[17],"emphasize":[18],"individual":[19],"client":[20,100],"demands":[21],"at":[22,70],"a":[23,39,71,74,80,109],"time,":[24],"such":[25],"as":[26],"enforcing":[27],"the":[28,65,89,122,134,166,175,187,205],"\u201cright":[29],"to":[30,63,86,203],"be":[31,180],"forgotten\u201d":[32],"or":[33,152],"mitigating":[34],"poor-quality":[35,93,169],"data":[36,94,170],"contributions":[37,66,154],"single":[40],"client.":[41],"These":[42],"strategies":[43],"frequently":[44],"rely":[45],"on":[46,144,156,174,195],"client-side":[47],"computational":[48],"engagement":[49],"during":[50],"unlearning.":[51],"However,":[52],"these":[53],"approaches":[54],"often":[55],"neglect":[56],"situations":[57],"in":[58],"which":[59],"it":[60],"is":[61],"crucial":[62],"unlearn":[64],"multiple":[68,96,172],"participants":[69,97,173],"time":[72],"global":[75,104,176],"model.":[76,177],"Therefore,":[77],"we":[78],"introduce":[79],"novel":[81,110],"FUL":[82],"method":[83],"named":[84],"\u201cFULClean\u201d":[85],"parallelly":[87],"erase":[88],"adverse":[90],"impacts":[91,167],"without":[98,182],"using":[99],"resources":[101],"and":[102,120,127,148,163,184,207],"maintaining":[103],"model":[105,142],"performance.":[106],"FULClean":[107,160],"employs":[108],"Contrastive":[111],"Threshold-based":[112],"Contribution":[113],"Rectification":[114],"(CTCR)":[115],"mechanism,":[116],"that":[117],"(1)":[118],"identifies":[119],"classifies":[121],"local":[123,130],"models":[124,202],"into":[125],"unaffected":[126],"potentially":[128,140],"affected":[129,141,158],"models,":[131],"(2)":[132],"computes":[133],"dynamic":[135],"contribution":[136],"threshold":[137],"for":[138],"each":[139],"based":[143,155],"layer-wise":[145],"parameter":[146],"divergence,":[147],"(3)":[149],"selectively":[150],"replaces":[151],"remove":[153],"their":[157],"ratio.":[159],"can":[161,179],"categorize":[162],"swiftly":[164],"eliminate":[165],"This":[178],"accomplished":[181],"communication":[183],"utilization":[185],"client's":[188],"resources.":[189],"Extensive":[190],"experiments":[191],"are":[192],"also":[193],"conducted":[194],"four":[196],"distinct":[197],"datasets":[198],"with":[199],"two":[200],"different":[201],"showcase":[204],"effectiveness":[206],"efficiency":[208],"FULClean.":[210]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
