{"id":"https://openalex.org/W4416184783","doi":"https://doi.org/10.48550/arxiv.2508.09866","title":"FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness","display_name":"FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness","publication_year":2025,"publication_date":"2025-08-13","ids":{"openalex":"https://openalex.org/W4416184783","doi":"https://doi.org/10.48550/arxiv.2508.09866"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2508.09866","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09866","pdf_url":"https://arxiv.org/pdf/2508.09866","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.09866","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062275158","display_name":"S. Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Siyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120596672","display_name":"Meng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Meng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397734","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0003-1068-6301"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066234112","display_name":"Ningning Ding","orcid":"https://orcid.org/0000-0002-9052-8719"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Ningning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.22360000014305115,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.22360000014305115,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.20059999823570251,"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":0.1664000004529953,"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/process","display_name":"Process (computing)","score":0.6093000173568726},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.5432000160217285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329000234603882},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.5432000160217285},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.40799999237060547},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.387800008058548},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3125},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.24240000545978546}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2508.09866","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09866","pdf_url":"https://arxiv.org/pdf/2508.09866","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ojs.aaai.org:article/39895","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/39895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2508.09866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.09866","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.09866","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09866","pdf_url":"https://arxiv.org/pdf/2508.09866","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"To":[0],"protect":[1],"clients'":[2],"right":[3],"to":[4,13,67,97,108],"be":[5],"forgotten":[6],"in":[7,112,126],"federated":[8,10,63],"learning,":[9],"unlearning":[11,33,50,64,86,89,103,130,152,164,181],"aims":[12],"remove":[14],"the":[15,22,37,61,79,100,162,178],"data":[16,163],"contribution":[17],"of":[18,40,102,128],"leaving":[19,144],"clients":[20,48],"from":[21,171],"global":[23],"learned":[24],"model.":[25],"While":[26],"current":[27],"studies":[28],"mainly":[29],"focused":[30],"on":[31],"enhancing":[32],"efficiency":[34,41,71],"and":[35,43,73,88,120,132,145,148,173],"effectiveness,":[36],"crucial":[38],"aspects":[39],"fairness":[42,45,72,101,115,125],"performance":[44,74,131],"among":[46,84,154],"decentralized":[47],"during":[49],"have":[51],"remained":[52],"largely":[53],"unexplored.":[54],"In":[55],"this":[56],"study,":[57],"we":[58,92,106],"introduce":[59],"FedShard,":[60],"first":[62],"algorithm":[65],"designed":[66],"concurrently":[68],"guarantee":[69],"both":[70,129],"fairness.":[75,90],"FedShard":[76,137,160],"adaptively":[77],"addresses":[78],"challenges":[80],"introduced":[81],"by":[82],"dilemmas":[83],"convergence,":[85],"efficiency,":[87],"Furthermore,":[91],"propose":[93],"two":[94],"novel":[95],"metrics":[96],"quantitatively":[98],"assess":[99],"algorithms,":[104],"which":[105],"prove":[107],"satisfy":[109],"well-known":[110],"properties":[111],"other":[113],"existing":[114],"measurements.":[116],"Our":[117],"theoretical":[118],"analysis":[119],"numerical":[121],"evaluation":[122],"validate":[123],"FedShard's":[124],"terms":[127],"efficiency.":[133],"We":[134],"demonstrate":[135],"that":[136,159],"mitigates":[138],"unfairness":[139],"risks":[140],"such":[141],"as":[142],"cascaded":[143],"poisoning":[146],"attacks":[147],"realizes":[149],"more":[150],"balanced":[151],"costs":[153],"clients.":[155],"Experimental":[156],"results":[157],"indicate":[158],"accelerates":[161],"process":[165],"1.3-6.2":[166],"times":[167,175],"faster":[168,176],"than":[169,177],"retraining":[170],"scratch":[172],"4.9":[174],"state-of-the-art":[179],"exact":[180],"methods.":[182]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}