{"id":"https://openalex.org/W4290948380","doi":"https://doi.org/10.1145/3534678.3539231","title":"FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients","display_name":"FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290948380","doi":"https://doi.org/10.1145/3534678.3539231"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539231","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539231","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5063733770","display_name":"Zaixi Zhang","orcid":"https://orcid.org/0000-0002-0380-6558"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zaixi Zhang","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032504910","display_name":"Xiaoyu Cao","orcid":"https://orcid.org/0000-0002-9403-2059"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoyu Cao","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101997385","display_name":"Jinyuan Jia","orcid":"https://orcid.org/0000-0003-4452-1396"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinyuan Jia","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009102659","display_name":"Neil Zhenqiang Gong","orcid":"https://orcid.org/0000-0002-9900-9309"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neil Zhenqiang Gong","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063733770"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":27.5541,"has_fulltext":false,"cited_by_count":281,"citation_normalized_percentile":{"value":0.99819791,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2545","last_page":"2555"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9993000030517578,"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":0.9993000030517578,"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/T11860","display_name":"HIV, Drug Use, Sexual Risk","score":0.9305999875068665,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.835795521736145},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5924310684204102},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.5916621685028076},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5877684354782104},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5791235566139221},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4720112979412079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3337896764278412}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.835795521736145},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5924310684204102},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5916621685028076},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5877684354782104},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5791235566139221},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4720112979412079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3337896764278412},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539231","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539231","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G8377316191","display_name":null,"funder_award_id":"2112562","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2614254310","https://openalex.org/W2912213068","https://openalex.org/W3138153888","https://openalex.org/W4214503147","https://openalex.org/W4229455429"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2087343574"],"abstract_inverted_index":{"Federated":[0],"learning":[1],"(FL)":[2],"is":[3,62,124],"vulnerable":[4],"to":[5,22,38,68,92,216],"model":[6,16,20,43,71,116,127,130,157,165,176,184,209],"poisoning":[7,72,128,210],"attacks,":[8],"in":[9,126,135,159,188,206],"which":[10,36],"malicious":[11,59,79,88,100,143,172,204,222],"clients":[12,47,101,144,205],"corrupt":[13],"the":[14,23,96,99,118,152,174,179,182,220],"global":[15,42,115,231],"via":[17,86,145],"sending":[18],"manipulated":[19],"updates":[21,131,166],"server.":[24],"Existing":[25],"defenses":[26],"mainly":[27],"rely":[28],"on":[29,163,194],"Byzantine-robust":[30,105,225],"or":[31,106],"provably":[32,107],"robust":[33,108],"FL":[34,109,226],"methods,":[35],"aim":[37],"learn":[39,112,229],"an":[40,64,113],"accurate":[41,114,230],"even":[44],"if":[45,173],"some":[46],"are":[48,138,186],"malicious.":[49],"However,":[50],"they":[51],"can":[52,111,201,228],"only":[53],"resist":[54],"a":[55,75,104,133,155,169],"small":[56],"number":[57,77],"of":[58,78,98],"clients.":[60,80,89,120],"It":[61],"still":[63],"open":[65],"challenge":[66,85],"how":[67],"defend":[69],"against":[70],"attacks":[73,211,214],"with":[74],"large":[76],"Our":[81,121,191],"FLDetector":[82,90,141,200],"addresses":[83],"this":[84],"detecting":[87],"aims":[91],"detect":[93,203],"and":[94,167,181,212],"remove":[95],"majority":[97],"such":[102],"that":[103,199],"method":[110],"using":[117],"remaining":[119],"key":[122],"observation":[123],"that,":[125],"attacks,the":[129],"from":[132,178],"client":[134,170,180],"multiple":[136,189,207],"iterations":[137],"inconsistent.":[139],"Therefore,":[140],"detects":[142],"checking":[146],"their":[147],"model-updates":[148],"consistency.":[149],"Roughly":[150],"speaking,":[151],"server":[153],"predicts":[154],"client's":[156],"update":[158,177,185],"each":[160],"iteration":[161],"based":[162],"historical":[164],"flags":[168],"as":[171],"received":[175],"predicted":[183],"inconsistent":[187],"iterations.":[190],"extensive":[192],"experiments":[193],"three":[195],"benchmark":[196],"datasets":[197],"show":[198],"accurately":[202],"state-of-the-art":[208],"adaptive":[213],"tailored":[215],"FLDetector.":[217],"After":[218],"removing":[219],"detected":[221],"clients,":[223],"existing":[224],"methods":[227],"models.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":17},{"year":2025,"cited_by_count":119},{"year":2024,"cited_by_count":94},{"year":2023,"cited_by_count":48},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2025-10-10T00:00:00"}
