{"id":"https://openalex.org/W7166336783","doi":"https://doi.org/10.1145/3801487.3801832","title":"EnsembleHealer: Autonomous Recovery from Model Poisoning in Decentralized Federated Learning","display_name":"EnsembleHealer: Autonomous Recovery from Model Poisoning in Decentralized Federated Learning","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7166336783","doi":"https://doi.org/10.1145/3801487.3801832"},"language":null,"primary_location":{"id":"doi:10.1145/3801487.3801832","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801487.3801832","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Computing Frontiers","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3801487.3801832","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005303754","display_name":"Srinija Ramichetty","orcid":null},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srinija Ramichetty","raw_affiliation_strings":["Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., USA"],"raw_orcid":"https://orcid.org/0009-0004-0257-2894","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139489273","display_name":"Mahmoud Abumandour","orcid":"https://orcid.org/0009-0004-9188-1882"},"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":"Mahmoud Abumandour","raw_affiliation_strings":["Simon Fraser University, Burnaby, British Columbia, Canada"],"raw_orcid":"https://orcid.org/0009-0004-9188-1882","affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, British Columbia, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072086494","display_name":"Alaa R. Alameldeen","orcid":"https://orcid.org/0000-0002-9064-4621"},"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":"Alaa R. Alameldeen","raw_affiliation_strings":["Simon Fraser University, Burnaby, British Columbia, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9064-4621","affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, British Columbia, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045879054","display_name":"Guru Venkataramani","orcid":"https://orcid.org/0000-0002-7084-7560"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guru Venkataramani","raw_affiliation_strings":["Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., USA"],"raw_orcid":"https://orcid.org/0000-0002-7084-7560","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93856038,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"299","last_page":"308"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.5666999816894531,"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":0.5666999816894531,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.2328999936580658,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.03720000013709068,"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/federated-learning","display_name":"Federated learning","score":0.7541000247001648},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5422999858856201},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.41769999265670776},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.3596999943256378},{"id":"https://openalex.org/keywords/distributed-database","display_name":"Distributed database","score":0.32589998841285706},{"id":"https://openalex.org/keywords/replica","display_name":"Replica","score":0.3174000084400177},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.30970001220703125}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7541000247001648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7527999877929688},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.6208999752998352},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5422999858856201},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.387800008058548},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.3596999943256378},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.35350000858306885},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C2775937380","wikidata":"https://www.wikidata.org/wiki/Q1232589","display_name":"Replica","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C205875254","wikidata":"https://www.wikidata.org/wiki/Q17156857","display_name":"Decentralised system","level":3,"score":0.3075000047683716},{"id":"https://openalex.org/C165136773","wikidata":"https://www.wikidata.org/wiki/Q1363179","display_name":"Single point of failure","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3801487.3801832","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801487.3801832","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Computing Frontiers","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3801487.3801832","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801487.3801832","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Computing Frontiers","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6534257166","display_name":null,"funder_award_id":"N00014-24-1-2046","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G7820216123","display_name":null,"funder_award_id":"CNS-2219180","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2071642320","https://openalex.org/W2122312526","https://openalex.org/W2963341956","https://openalex.org/W2970602317","https://openalex.org/W3097817814","https://openalex.org/W3137147200","https://openalex.org/W4205738602","https://openalex.org/W4226461837","https://openalex.org/W4240522432","https://openalex.org/W4381163583","https://openalex.org/W4382203046","https://openalex.org/W4386607611","https://openalex.org/W4396890578","https://openalex.org/W4399168337","https://openalex.org/W4400680802","https://openalex.org/W4403760224","https://openalex.org/W4404517243","https://openalex.org/W4404848788","https://openalex.org/W4406269727","https://openalex.org/W4406697365","https://openalex.org/W4407393507","https://openalex.org/W4408120180","https://openalex.org/W4409812826","https://openalex.org/W4414547300","https://openalex.org/W4414611598","https://openalex.org/W4417504533","https://openalex.org/W7092191408"],"related_works":[],"abstract_inverted_index":{"Decentralized":[0],"Federated":[1],"Learning":[2],"(DFL)":[3],"enables":[4],"collaborative":[5],"training":[6],"across":[7],"distributed":[8,80],"nodes":[9,126,137],"without":[10],"central":[11],"coordination,":[12],"but":[13,53],"it":[14],"also":[15],"exposes":[16],"new":[17],"attack":[18,43],"surfaces:":[19],"a":[20,41,45,66,98,117],"single":[21,118],"compromised":[22,94,119,125],"node":[23],"can":[24,107],"poison":[25],"updates":[26],"and":[27,72,86,92,140],"degrade":[28],"global":[29],"performance.":[30],"In":[31],"this":[32,61],"paper,":[33],"we":[34,63],"demonstrate":[35],"Quantization-aware":[36],"Distortion":[37],"(QuAD)":[38],"for":[39],"DFL,":[40],"model-poisoning":[42],"where":[44],"malicious":[46],"model":[47,95],"appears":[48],"benign":[49],"in":[50,97,132],"full":[51],"precision":[52],"triggers":[54],"harmful":[55],"behavior":[56],"after":[57],"quantization.":[58],"To":[59],"mitigate":[60],"threat,":[62],"propose":[64],"EnsembleHealer,":[65],"decentralized":[67,100],"recovery":[68,143],"framework":[69],"that":[70,105],"detects":[71],"isolates":[73],"poisoned":[74],"or":[75],"degraded":[76],"models":[77],"through":[78],"latency-aware":[79],"agreement.":[81],"EnsembleHealer":[82,121],"combines":[83],"application-level":[84],"constraints":[85],"dynamic":[87],"quorum":[88],"selection":[89],"to":[90,148],"detect":[91],"isolate":[93],"copies":[96],"fully":[99],"setting.":[101],"Our":[102],"experiments":[103],"show":[104],"QuAD":[106],"reduce":[108],"the":[109,124,129,136,149],"federation\u2019s":[110],"overall":[111],"accuracy":[112],"by":[113,127,138,144],"9%":[114],"even":[115],"with":[116],"node.":[120],"efficiently":[122],"identifies":[123],"cutting":[128],"costs":[130],"involved":[131],"agreement":[133],"building":[134],"between":[135],"50%,":[139],"improves":[141],"packet-loss":[142],"10":[145],"\u00d7":[146],"relative":[147],"Paxos":[150],"algorithm.":[151]},"counts_by_year":[],"updated_date":"2026-06-28T06:20:29.262167","created_date":"2026-06-28T00:00:00"}