{"id":"https://openalex.org/W4285813885","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824996","title":"Heterogeneity-Aware Federated Learning for Device Anomaly Detection in Industrial loT","display_name":"Heterogeneity-Aware Federated Learning for Device Anomaly Detection in Industrial loT","publication_year":2022,"publication_date":"2022-05-30","ids":{"openalex":"https://openalex.org/W4285813885","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824996"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc55113.2022.9824996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824996","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","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":"2022 International Wireless Communications and Mobile Computing (IWCMC)","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/A5086747236","display_name":"Zhuoer Hu","orcid":"https://orcid.org/0000-0002-4114-2983"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhuoer Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China","Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011351180","display_name":"Yueming Lu","orcid":"https://orcid.org/0000-0003-3196-0349"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueming Lu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China","Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101654837","display_name":"Hui Gao","orcid":"https://orcid.org/0000-0003-4206-0370"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Gao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China","Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059494893","display_name":"Wenjun Xu","orcid":"https://orcid.org/0000-0001-8767-4742"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjun Xu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China","Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086747236"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.05937152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"653","last_page":"659"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994999766349792,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.750597357749939},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.632503867149353},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.612464964389801},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.5877321362495422},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5273550152778625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4548417329788208},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.4403989911079407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3712632656097412},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33046993613243103}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.750597357749939},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.632503867149353},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.612464964389801},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.5877321362495422},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5273550152778625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4548417329788208},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.4403989911079407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3712632656097412},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33046993613243103},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc55113.2022.9824996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824996","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","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":"2022 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6200000047683716}],"awards":[{"id":"https://openalex.org/G3271096445","display_name":null,"funder_award_id":"2019YFB2102404,2019YFB2102403","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2038819732","https://openalex.org/W2535838896","https://openalex.org/W2797742547","https://openalex.org/W2799758613","https://openalex.org/W2846676623","https://openalex.org/W2974175488","https://openalex.org/W2980960237","https://openalex.org/W2981948158","https://openalex.org/W2991544738","https://openalex.org/W3021654819","https://openalex.org/W3033040110","https://openalex.org/W3043384436","https://openalex.org/W3043527956","https://openalex.org/W3044239919","https://openalex.org/W3084170757","https://openalex.org/W3093606573","https://openalex.org/W3105750153","https://openalex.org/W3113308842","https://openalex.org/W3122864121","https://openalex.org/W3164952570","https://openalex.org/W4297687186","https://openalex.org/W4318619660","https://openalex.org/W6728757088"],"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/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"With":[0],"the":[1,6,20,27,34,107,139],"popularity":[2],"and":[3,33,50,76,85,93,112,146],"application":[4],"of":[5,9,19,30,37],"Industrial":[7],"Internet":[8],"Things":[10],"(1IoT),":[11],"device":[12,31,52,87],"anomaly":[13,53,65,88],"detection":[14,54,66,89],"is":[15,91,101],"considered":[16],"as":[17],"one":[18],"important":[21],"challenges":[22],"in":[23,55,109,131],"IloT":[24,38,56,69,117],"implementation.":[25],"However,":[26],"privacy":[28],"sensitivity":[29],"data":[32,113],"high":[35],"heterogeneity":[36],"devices":[39,121],"make":[40],"it":[41],"impossible":[42],"for":[43,68],"traditional":[44],"schemes":[45],"to":[46,125],"achieve":[47,143],"efficient,":[48],"accurate,":[49],"privacy-protected":[51],"networks.":[57,134],"In":[58,80],"this":[59],"study,":[60],"we":[61],"propose":[62],"an":[63,82],"intelligent":[64],"architecture":[67],"networks":[70],"based":[71],"on":[72],"federated":[73,96],"optimization":[74],"algorithms":[75],"deep":[77],"learning":[78,97],"(DL).":[79],"particular,":[81],"online,":[83],"adaptive,":[84],"semi-supervised":[86],"model":[90,130],"designed,":[92],"a":[94,127],"heterogeneity-aware":[95],"algorithm,":[98],"called":[99],"Clustered-FedProx,":[100],"presented.":[102],"The":[103],"Clustered-FedProx":[104],"algorithm":[105],"considers":[106],"differences":[108],"computational":[110],"power":[111],"statistical":[114],"distribution":[115],"among":[116],"devices,":[118],"whereby":[119],"multiple":[120],"can":[122,142],"be":[123],"coordinated":[124],"train":[126],"global":[128],"DL":[129],"highly":[132],"heterogeneous":[133],"Simulation":[135],"results":[136],"show":[137],"that":[138],"proposed":[140],"scheme":[141],"more":[144],"stable":[145],"accurate":[147],"performance":[148],"than":[149],"conventional":[150],"schemes.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
