{"id":"https://openalex.org/W7131893892","doi":"https://doi.org/10.1109/tbdata.2026.3668538","title":"Survey on Federated Unlearning: Challenges and Opportunities","display_name":"Survey on Federated Unlearning: Challenges and Opportunities","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7131893892","doi":"https://doi.org/10.1109/tbdata.2026.3668538"},"language":null,"primary_location":{"id":"doi:10.1109/tbdata.2026.3668538","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2026.3668538","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-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/A5068301566","display_name":"Hyejun Jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I177605424","display_name":"Amherst College","ror":"https://ror.org/028vqfs63","country_code":"US","type":"education","lineage":["https://openalex.org/I177605424"]},{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hyejun Jeong","raw_affiliation_strings":["UMass Amherst, Amherst, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-0344-2387","affiliations":[{"raw_affiliation_string":"UMass Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I177605424","https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109689136","display_name":"Shiqing Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I177605424","display_name":"Amherst College","ror":"https://ror.org/028vqfs63","country_code":"US","type":"education","lineage":["https://openalex.org/I177605424"]},{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiqing Ma","raw_affiliation_strings":["UMass Amherst, Amherst, MA, USA"],"raw_orcid":"https://orcid.org/0000-0003-1551-8948","affiliations":[{"raw_affiliation_string":"UMass Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I177605424","https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018588864","display_name":"Amir Houmansadr","orcid":"https://orcid.org/0000-0002-7553-6657"},"institutions":[{"id":"https://openalex.org/I177605424","display_name":"Amherst College","ror":"https://ror.org/028vqfs63","country_code":"US","type":"education","lineage":["https://openalex.org/I177605424"]},{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amir Houmansadr","raw_affiliation_strings":["UMass Amherst, Amherst, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7553-6657","affiliations":[{"raw_affiliation_string":"UMass Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I177605424","https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":32.8932,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.99010008,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"12","issue":"3","first_page":"702","last_page":"720"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13566","display_name":"Global Educational Reforms and Inequalities","score":0.03009999915957451,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"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/T13566","display_name":"Global Educational Reforms and Inequalities","score":0.03009999915957451,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"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/T12171","display_name":"Open Education and E-Learning","score":0.0203000009059906,"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"}},{"id":"https://openalex.org/T13194","display_name":"ICT in Developing Communities","score":0.016699999570846558,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/context","display_name":"Context (archaeology)","score":0.5371000170707703},{"id":"https://openalex.org/keywords/open-research","display_name":"Open research","score":0.4713999927043915},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4666000008583069},{"id":"https://openalex.org/keywords/data-sharing","display_name":"Data sharing","score":0.43619999289512634},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4300000071525574},{"id":"https://openalex.org/keywords/foregrounding","display_name":"Foregrounding","score":0.4198000133037567},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.41850000619888306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8026000261306763},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.593999981880188},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5371000170707703},{"id":"https://openalex.org/C2778464652","wikidata":"https://www.wikidata.org/wiki/Q309849","display_name":"Open research","level":2,"score":0.4713999927043915},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.43619999289512634},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4300000071525574},{"id":"https://openalex.org/C2777425269","wikidata":"https://www.wikidata.org/wiki/Q5468194","display_name":"Foregrounding","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3621000051498413},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C2776604539","wikidata":"https://www.wikidata.org/wiki/Q6423395","display_name":"Knowledge sharing","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2597000002861023},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C20136886","wikidata":"https://www.wikidata.org/wiki/Q749647","display_name":"Interoperability","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2026.3668538","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2026.3668538","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41706597805023193,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1,8],"(FL),":[2],"introduced":[3],"in":[4,42,56,102,182],"2017,":[5],"enables":[6],"collaborative":[7],"across":[9,131],"mutually":[10],"distrusting":[11],"parties":[12],"without":[13],"sharing":[14],"raw":[15],"data,":[16,37],"enabling":[17],"privacy-preserving":[18],"model":[19],"training.":[20],"However,":[21],"emerging":[22],"regulations":[23],"and":[24,70,112,127,136,144,170,173,180,198],"practical":[25],"demands":[26],"require":[27],"models":[28],"to":[29,32,39,66,161,188],"be":[30],"able":[31],"<italic":[33,82,92],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[34,83,93],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">forget</i>":[35],"learned":[36],"leading":[38],"growing":[40],"interest":[41],"Machine":[43],"Unlearning":[44],"(MU).":[45],"In":[46],"the":[47,90],"context":[48],"of":[49,81,96],"FL,":[50],"many":[51],"techniques":[52],"developed":[53],"for":[54],"unlearning":[55,132,181],"centralized":[57,155],"settings":[58],"are":[59],"not":[60],"trivially":[61],"applicable.":[62],"This":[63,74,87],"is":[64],"due":[65],"interactivity,":[67],"stochasticity,":[68],"heterogeneity,":[69],"limited":[71],"data":[72,171],"accessibility.":[73],"has":[75],"motivated":[76],"a":[77,118],"distinct":[78],"research":[79,191],"area":[80],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">federated":[84],"unlearning</i>":[85],"(FU).":[86],"survey":[88,186],"provides":[89],"first":[91],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">practice-oriented":[94],"synthesis</i>":[95],"FU,":[97,162],"foregrounding":[98],"aspects":[99],"often":[100],"overlooked":[101],"prior":[103],"surveys:":[104],"data-distribution":[105],"modeling":[106],"(and":[107],"non-IID":[108],"simulation),":[109],"dataset":[110],"selection,":[111],"FL":[113],"system":[114,196],"configurations.":[115],"We":[116,157],"introduce":[117],"taxonomy":[119],"that":[120,151],"separates":[121],"influence":[122],"removal":[123],"from":[124,154],"performance":[125],"recovery,":[126],"compare":[128],"FU":[129,153,190],"approaches":[130],"targets,":[133],"aggregation":[134],"assumptions,":[135,197],"reproducibility":[137],"signals.":[138],"By":[139],"analyzing":[140],"datasets,":[141],"evaluation":[142],"metrics,":[143],"code":[145],"availability,":[146],"we":[147],"surface":[148],"key":[149],"trends":[150],"differentiate":[152],"MU.":[156],"highlight":[158],"challenges":[159],"unique":[160],"such":[163],"as":[164],"interactive":[165],"training,":[166],"stochastic":[167],"client":[168],"participation,":[169],"isolation,":[172],"synthesize":[174],"open":[175],"problems":[176],"around":[177],"scalability,":[178],"fairness,":[179],"foundation":[183],"models.":[184],"Our":[185],"aims":[187],"guide":[189],"by":[192],"clarifying":[193],"methodological":[194],"gaps,":[195],"promising":[199],"directions.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-28T00:00:00"}