{"id":"https://openalex.org/W4416251725","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228237","title":"Towards An Unsupervised Federated Hypernetwork Method for Distributed Anomaly Identification in Industrial IoT Environment","display_name":"Towards An Unsupervised Federated Hypernetwork Method for Distributed Anomaly Identification in Industrial IoT Environment","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251725","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228237"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5111193002","display_name":"Junfeng Hao","orcid":"https://orcid.org/0009-0009-6100-3436"},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junfeng Hao","raw_affiliation_strings":["Xihua University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Xihua University,Chengdu,China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338429","display_name":"Peng Chen","orcid":"https://orcid.org/0000-0001-5221-3655"},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Chen","raw_affiliation_strings":["Xihua University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Xihua University,Chengdu,China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100747334","display_name":"Xi Li","orcid":"https://orcid.org/0000-0001-8986-7601"},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Li","raw_affiliation_strings":["Xihua University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Xihua University,Chengdu,China","institution_ids":["https://openalex.org/I102345215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111193002"],"corresponding_institution_ids":["https://openalex.org/I102345215"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19489213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9164000153541565,"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":0.9164000153541565,"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":0.030700000002980232,"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"}},{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.011599999852478504,"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/anomaly-detection","display_name":"Anomaly detection","score":0.775600016117096},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6614999771118164},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.48260000348091125},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.47699999809265137},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.44620001316070557},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4262999892234802}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.775600016117096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336000204086304},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6614999771118164},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5717999935150146},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4487000107765198},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36880001425743103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36640000343322754},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3345000147819519},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.3301999866962433},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W61851215","https://openalex.org/W2157511636","https://openalex.org/W2982426954","https://openalex.org/W3004207920","https://openalex.org/W3044515030","https://openalex.org/W3182158470","https://openalex.org/W4293812121","https://openalex.org/W4302423442","https://openalex.org/W4316116055","https://openalex.org/W4372338130","https://openalex.org/W4377713808","https://openalex.org/W4379382413","https://openalex.org/W4384405398","https://openalex.org/W4385245566","https://openalex.org/W4385662192","https://openalex.org/W4386066601","https://openalex.org/W4389617273","https://openalex.org/W4396795653","https://openalex.org/W4398177832"],"related_works":[],"abstract_inverted_index":{"Distributed":[0],"anomaly":[1,119,123,134,138],"detection":[2,9,14],"for":[3,38],"industrial":[4,26,62,67],"IoT":[5,27,40,63],"is":[6,175],"crucial.":[7],"Traditional":[8],"methods":[10],"suffer":[11],"from":[12],"degraded":[13],"performance":[15],"due":[16,86],"to":[17,43,87,116],"the":[18,25,50,74,83,94,100,107,118,126,152,162,168,181],"data":[19,57,69],"heterogeneity":[20,58],"and":[21,59,110,130,161],"high":[22],"dynamics":[23],"in":[24,61,148],"environment.":[28],"We":[29,140],"propose":[30],"an":[31,142],"Unsupervised":[32],"Federated":[33],"Hypernetwork":[34],"Anomaly":[35],"Identification":[36],"Method":[37],"Industrial":[39],"Environment":[41],"(uFedHyAI)":[42],"address":[44],"these":[45],"issues.":[46],"Specifically,":[47],"we":[48,72],"introduce":[49],"federated":[51],"hypernetwork":[52],"architecture,":[53],"which":[54,81,149],"effectively":[55],"mitigates":[56],"volatility":[60],"environments":[64],"while":[65,136],"protecting":[66],"device":[68],"privacy.":[70],"Then,":[71],"employ":[73],"Sequence":[75],"Transformation":[76],"Normalization":[77],"Transformer":[78],"(SC":[79],"Nor-Transformer),":[80],"addresses":[82],"timing":[84,101],"bias":[85],"model":[88],"aggregation":[89],"through":[90],"sequence":[91,97],"transformation.":[92],"At":[93],"same":[95],"time,":[96],"normalization":[98],"improves":[99],"dependency":[102],"of":[103,133,173,180],"captured":[104],"subsequences.":[105],"Finally,":[106],"subsequences\u2019":[108],"temporal":[109],"frequency":[111],"domain":[112],"errors":[113],"are":[114],"combined":[115],"form":[117],"score.":[120],"This":[121],"novel":[122],"score":[124,158],"enhances":[125],"autocorrelation":[127],"between":[128],"subsequences":[129],"performs":[131],"localization":[132,170],"sensors":[135],"achieving":[137],"detection.":[139],"performed":[141],"extensive":[143],"evaluation":[144],"on":[145],"six":[146],"datasets,":[147],"uFedHyAI":[150,174],"outperformed":[151],"existing":[153],"state-of-the-art":[154],"baseline":[155,183],"average":[156,163,169],"F1":[157],"by":[159,165],"6.59%":[160],"AUROC":[164],"3.56%.":[166],"Moreover,":[167],"fault":[171],"accuracy":[172],"9.23%":[176],"higher":[177],"than":[178],"that":[179],"optimal":[182],"method.":[184]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
