{"id":"https://openalex.org/W4411552676","doi":"https://doi.org/10.1109/cscwd64889.2025.11033546","title":"FLUDP: A Backdoor Attack Defense Framework in Federated Learning","display_name":"FLUDP: A Backdoor Attack Defense Framework in Federated Learning","publication_year":2025,"publication_date":"2025-05-05","ids":{"openalex":"https://openalex.org/W4411552676","doi":"https://doi.org/10.1109/cscwd64889.2025.11033546"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd64889.2025.11033546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","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/A5024628268","display_name":"Caicun Zhou","orcid":"https://orcid.org/0000-0002-6958-0766"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Caisong Zhou","raw_affiliation_strings":["School of computer science and technology, Xinjiang University,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"School of computer science and technology, Xinjiang University,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104204469","display_name":"Yanqing Yang","orcid":"https://orcid.org/0009-0003-9991-8275"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanqing Yang","raw_affiliation_strings":["School of computer science and technology, Xinjiang University,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"School of computer science and technology, Xinjiang University,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100647916","display_name":"Lin\u2010Lin Zhang","orcid":"https://orcid.org/0000-0002-0509-1138"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlin Zhang","raw_affiliation_strings":["School of Software, Xinjiang University,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"School of Software, Xinjiang University,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024628268"],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07865272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1826","last_page":"1831"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9883999824523926,"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":0.9883999824523926,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9819999933242798,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9650999903678894,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/backdoor","display_name":"Backdoor","score":0.9952541589736938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7014620900154114},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.6336327791213989}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.9952541589736938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7014620900154114},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6336327791213989}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd64889.2025.11033546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2551176409","https://openalex.org/W2990595670","https://openalex.org/W3087391814","https://openalex.org/W3175919946","https://openalex.org/W4221129260","https://openalex.org/W4281398987","https://openalex.org/W4385187226","https://openalex.org/W4385573575","https://openalex.org/W4392931300","https://openalex.org/W4393396274","https://openalex.org/W6728757088","https://openalex.org/W6743821447","https://openalex.org/W6748786018","https://openalex.org/W6752600739","https://openalex.org/W6771533808","https://openalex.org/W6780640148","https://openalex.org/W6787633081","https://openalex.org/W6799246147","https://openalex.org/W6810562672","https://openalex.org/W7056673059"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4320031223","https://openalex.org/W4200629851","https://openalex.org/W4281902577","https://openalex.org/W4309417370","https://openalex.org/W4292107232","https://openalex.org/W3009072493","https://openalex.org/W4401407399"],"abstract_inverted_index":{"Federated":[0],"learning(FL)":[1],"is":[2,19,104],"a":[3,139,163],"popular":[4],"multi-party":[5],"collaborative":[6],"privacy":[7],"computing":[8],"method.":[9],"Due":[10],"to":[11,22,91,168],"the":[12,28,32,37,41,46,56,61,123,130],"distributed":[13],"characteristics":[14],"of":[15,40],"its":[16],"client,":[17],"it":[18],"more":[20],"vulnerable":[21],"backdoor":[23,67,94],"attacks.":[24],"In":[25],"this":[26],"attack,":[27],"attacker":[29],"will":[30],"inject":[31],"manipulated":[33],"model":[34,49,77],"update":[35],"into":[36],"aggregation":[38],"process":[39],"federated":[42],"model,":[43],"so":[44],"that":[45,99],"final":[47],"generated":[48],"can":[50],"provide":[51],"targeted":[52],"wrong":[53],"prediction":[54],"for":[55,109],"specific":[57],"input":[58],"selected":[59],"by":[60],"attacker.":[62],"Current":[63],"defense":[64,80,92,170],"methods":[65,81],"against":[66,93],"attacks":[68,95],"mainly":[69],"focus":[70],"on":[71,148],"detecting":[72],"and":[73,102,138,151,158,161],"filtering":[74],"malicious":[75],"client":[76],"updates.":[78],"These":[79],"adopt":[82],"HDBSCAN":[83],"clustering,":[84,86],"K-means":[85],"etc,":[87],"which":[88,126],"generally":[89],"fail":[90],"in":[96],"decoy":[97],"models":[98],"contain":[100],"indicators":[101],"there":[103],"no":[105],"obvious":[106],"clipping":[107,113,142],"threshold":[108,141],"benign":[110],"groups":[111],"further":[112],"after":[114],"clustering":[115,136],"defense.":[116],"To":[117],"address":[118],"these":[119],"shortcomings,":[120],"we":[121],"propose":[122],"FLUDP":[124,147,154],"method,":[125],"uses":[127],"UMAP":[128],"Magnify":[129],"angular":[131],"difference":[132],"between":[133],"updates,":[134],"DBSCAN":[135],"technology,":[137],"clear":[140],"technique.":[143],"We":[144],"have":[145],"evaluated":[146],"MNIST,":[149],"Tiny-Imagenet,":[150],"CIFAR10":[152],"datasets.":[153],"achieves":[155],"lower":[156],"ABA":[157],"EBA":[159],"rates":[160],"maintains":[162],"good":[164],"MA":[165],"rate":[166],"compared":[167],"previous":[169],"methods.":[171]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
