{"id":"https://openalex.org/W7151555185","doi":"https://doi.org/10.1109/icmla66185.2025.00067","title":"MADE-PI: Framework for Effective Anomaly Detection in Federated Learning Applications","display_name":"MADE-PI: Framework for Effective Anomaly Detection in Federated Learning Applications","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151555185","doi":"https://doi.org/10.1109/icmla66185.2025.00067"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5133136322","display_name":"Raman Zatsarenko","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Raman Zatsarenko","raw_affiliation_strings":["Rochester Institute of Technology,Rochester,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology,Rochester,NY,USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004467826","display_name":"Sergey Chuprov","orcid":"https://orcid.org/0000-0001-7081-8797"},"institutions":[{"id":"https://openalex.org/I2802326326","display_name":"The University of Texas Rio Grande Valley","ror":"https://ror.org/02p5xjf12","country_code":"US","type":"education","lineage":["https://openalex.org/I2802326326"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sergei Chuprov","raw_affiliation_strings":["The University of Texas Rio Grande Valley,Edinburg,TX,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas Rio Grande Valley,Edinburg,TX,USA","institution_ids":["https://openalex.org/I2802326326"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133092976","display_name":"Dmitrii Korobeinikov","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitrii Korobeinikov","raw_affiliation_strings":["Rochester Institute of Technology,Rochester,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology,Rochester,NY,USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018260949","display_name":"Leon Reznik","orcid":"https://orcid.org/0000-0003-4622-220X"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leon Reznik","raw_affiliation_strings":["Rochester Institute of Technology,Rochester,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology,Rochester,NY,USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125088920","display_name":"Adrian Pe\u00f1a","orcid":null},"institutions":[{"id":"https://openalex.org/I2802326326","display_name":"The University of Texas Rio Grande Valley","ror":"https://ror.org/02p5xjf12","country_code":"US","type":"education","lineage":["https://openalex.org/I2802326326"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adrian Pe\u00f1a","raw_affiliation_strings":["The University of Texas Rio Grande Valley,Edinburg,TX,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas Rio Grande Valley,Edinburg,TX,USA","institution_ids":["https://openalex.org/I2802326326"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5133136322"],"corresponding_institution_ids":["https://openalex.org/I155173764"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86187835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"446","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.27489998936653137,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.27489998936653137,"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":0.11969999969005585,"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"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0786999985575676,"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/anomaly-detection","display_name":"Anomaly detection","score":0.4790000021457672},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3499999940395355},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.32820001244544983},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.30469998717308044},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.28529998660087585},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.27090001106262207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6787999868392944},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4790000021457672},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3953999876976013},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":15,"referenced_works":["https://openalex.org/W2067400662","https://openalex.org/W2909847702","https://openalex.org/W3087391814","https://openalex.org/W3138153888","https://openalex.org/W3138597937","https://openalex.org/W3171189420","https://openalex.org/W4229083957","https://openalex.org/W4285158235","https://openalex.org/W4297521542","https://openalex.org/W4309027110","https://openalex.org/W4317436377","https://openalex.org/W4320481068","https://openalex.org/W4390189929","https://openalex.org/W4391524120","https://openalex.org/W4408222477"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"research,":[2],"we":[3],"address":[4],"a":[5,27,34,53,64,71,81,90,149],"critical":[6],"vulnerability":[7],"of":[8,89,107,132,153],"Federated":[9],"Learning":[10],"(FL)":[11],"in":[12,46],"practical":[13],"applications:":[14],"their":[15],"susceptibility":[16],"to":[17,39],"adversarial":[18],"attacks":[19],"from":[20],"malicious":[21,43,116,166],"participants,":[22],"which":[23],"can":[24],"severely":[25],"degrade":[26],"global":[28],"model\u2019s":[29],"performance.":[30],"We":[31,118,143],"propose":[32],"MADE-PI,":[33],"novel":[35],"defense":[36],"mechanism":[37],"designed":[38],"identify":[40],"and":[41,93,114,140,169,174],"exclude":[42],"clients":[44],"early":[45],"the":[47,57,77,86,105],"training":[48],"process.":[49],"Our":[50],"method":[51],"introduces":[52],"pre-aggregation":[54],"step":[55],"where":[56,80],"server":[58],"evaluates":[59],"each":[60],"client":[61,167],"update":[62],"using":[63],"Proportional-Integral":[65],"(PI)":[66],"score.":[67],"This":[68],"score":[69],"is":[70],"unique":[72],"distance-based":[73],"metric":[74],"inspired":[75],"by":[76,163],"PID":[78],"controller,":[79],"proportional":[82],"term":[83,96],"P":[84],"measures":[85],"immediate":[87],"deviation":[88],"client\u2019s":[91],"update,":[92],"an":[94,123],"integral":[95],"I":[97],"tracks":[98],"its":[99],"behavior":[100],"over":[101],"time.":[102],"By":[103],"analyzing":[104],"distribution":[106],"these":[108],"scores,":[109],"our":[110,120],"technique":[111],"effectively":[112],"flags":[113],"removes":[115],"contributions.":[117],"validate":[119],"approach":[121],"through":[122],"empirical":[124],"study":[125],"on":[126,145],"three":[127],"datasets":[128],"representing":[129],"various":[130],"applications":[131],"FL,":[133],"including":[134],"intelligent":[135],"transportation,":[136],"medical":[137],"image":[138],"analysis,":[139],"handwriting":[141],"recognition.":[142],"concentrate":[144],"data":[146],"poisoning":[147],"as":[148],"highly":[150],"relevant":[151],"class":[152],"attacks.":[154],"The":[155],"results":[156],"demonstrate":[157],"that":[158],"MADE-PI":[159],"outperforms":[160],"conventional":[161],"defenses":[162],"providing":[164],"superior":[165],"detection":[168],"improving":[170],"both":[171],"learning":[172],"efficiency":[173],"final":[175],"model":[176],"accuracy.":[177]},"counts_by_year":[],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2026-04-08T00:00:00"}
