{"id":"https://openalex.org/W4410341453","doi":"https://doi.org/10.1109/icnc64010.2025.10993643","title":"Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case","display_name":"Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case","publication_year":2025,"publication_date":"2025-02-17","ids":{"openalex":"https://openalex.org/W4410341453","doi":"https://doi.org/10.1109/icnc64010.2025.10993643"},"language":"en","primary_location":{"id":"doi:10.1109/icnc64010.2025.10993643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc64010.2025.10993643","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 Computing, Networking and Communications (ICNC)","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/A5062587761","display_name":"William Marfo","orcid":"https://orcid.org/0009-0003-0631-2750"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"William Marfo","raw_affiliation_strings":["University of Texas at El Paso,Department of Computer Science,El Paso,USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at El Paso,Department of Computer Science,El Paso,USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065745202","display_name":"Deepak K. Tosh","orcid":"https://orcid.org/0000-0001-8492-1066"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deepak K. Tosh","raw_affiliation_strings":["University of Texas at El Paso,Department of Computer Science,El Paso,USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at El Paso,Department of Computer Science,El Paso,USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041037945","display_name":"Shirley Moore","orcid":"https://orcid.org/0009-0009-1351-2797"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shirley V. Moore","raw_affiliation_strings":["University of Texas at El Paso,Department of Computer Science,El Paso,USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at El Paso,Department of Computer Science,El Paso,USA","institution_ids":["https://openalex.org/I164936912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062587761"],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":null,"apc_paid":null,"fwci":13.124,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.98582504,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"601","last_page":"605"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9998999834060669,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/computer-science","display_name":"Computer science","score":0.7393105030059814},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7029038071632385},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5840895175933838},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.49101901054382324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3748821020126343},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3415765166282654},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.322975218296051}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7393105030059814},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7029038071632385},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5840895175933838},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.49101901054382324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3748821020126343},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3415765166282654},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.322975218296051},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc64010.2025.10993643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc64010.2025.10993643","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 Computing, Networking and Communications (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2296509296","https://openalex.org/W4320031216","https://openalex.org/W4385338519","https://openalex.org/W4385568263","https://openalex.org/W4386943870","https://openalex.org/W4387389747","https://openalex.org/W4391097714","https://openalex.org/W4392910504","https://openalex.org/W4398150533","https://openalex.org/W4400644209","https://openalex.org/W4401011224","https://openalex.org/W6857257154"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"has":[3],"become":[4],"a":[5,61,102,118,124,159],"ubiquitous":[6],"approach":[7,76],"for":[8,65],"training":[9,128],"machine":[10],"learning":[11],"models":[12],"on":[13,32,85,101],"decentralized":[14],"data,":[15],"addressing":[16],"the":[17,27,79,109,136,175],"myriad":[18],"privacy":[19,38,52,70,92,139,146],"concerns":[20],"inherent":[21],"in":[22,121,127],"traditional":[23],"centralized":[24],"methods.":[25],"However,":[26],"efficiency":[28],"of":[29,81,177],"FL":[30,66],"depends":[31],"effective":[33],"client":[34,42,62,166],"selection":[35,43,63],"and":[36,71,88,111,123,141,150],"robust":[37],"preservation":[39],"mechanisms.":[40],"Inadequate":[41],"may":[44],"lead":[45],"to":[46,117,131,165],"suboptimal":[47],"model":[48,86,142],"performance,":[49,143],"while":[50,157],"insufficient":[51],"measures":[53],"risk":[54],"exposing":[55],"sensitive":[56],"data.":[57],"This":[58],"paper":[59],"proposes":[60],"framework":[64],"that":[67],"integrates":[68],"differential":[69],"fault":[72,154],"tolerance.":[73],"Our":[74,153],"adaptive":[75],"dynamically":[77],"adjusts":[78],"number":[80],"selected":[82],"clients":[83],"based":[84],"performance":[87,161],"system":[89],"constraints,":[90],"ensuring":[91],"through":[93],"calibrated":[94],"noise":[95,149],"addition.":[96],"We":[97],"evaluate":[98],"our":[99],"method":[100],"network":[103],"anomaly":[104],"detection":[105],"use":[106],"case":[107],"using":[108,170],"UNSW-NB15":[110],"ROAD":[112],"datasets.":[113],"Results":[114],"show":[115],"up":[116],"7%":[119],"increase":[120],"accuracy":[122],"25%":[125],"reduction":[126],"time":[129],"compared":[130],"FedL2P.":[132],"Moreover,":[133],"we":[134],"highlight":[135],"trade-offs":[137],"between":[138],"budgets":[140,147],"with":[144],"higher":[145],"reducing":[148],"improving":[151],"accuracy.":[152],"tolerance":[155],"mechanism,":[156],"causing":[158],"slight":[160],"drop,":[162],"enhances":[163],"robustness":[164],"failures.":[167],"Statistical":[168],"validation":[169],"Mann-Whitney":[171],"U":[172],"tests":[173],"confirms":[174],"significance":[176],"these":[178],"improvements":[179],"(p":[180],"<":[181],"0.05)<sup":[182],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[183],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}