{"id":"https://openalex.org/W3194606324","doi":"https://doi.org/10.1109/ipccc51483.2021.9679367","title":"Federated Variational Learning for Anomaly Detection in Multivariate Time Series","display_name":"Federated Variational Learning for Anomaly Detection in Multivariate Time Series","publication_year":2021,"publication_date":"2021-10-29","ids":{"openalex":"https://openalex.org/W3194606324","doi":"https://doi.org/10.1109/ipccc51483.2021.9679367","mag":"3194606324"},"language":"en","primary_location":{"id":"doi:10.1109/ipccc51483.2021.9679367","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipccc51483.2021.9679367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://commons.erau.edu/publication/1768","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100323882","display_name":"Kai Zhang","orcid":"https://orcid.org/0000-0001-5771-7422"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Zhang","raw_affiliation_strings":["Embry-Riddle Aeronautical University,Daytona Beach,FL,32114"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embry-Riddle Aeronautical University,Daytona Beach,FL,32114","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006087820","display_name":"Yushan Jiang","orcid":"https://orcid.org/0000-0002-4226-7534"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yushan Jiang","raw_affiliation_strings":["Embry-Riddle Aeronautical University,Daytona Beach,FL,32114"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embry-Riddle Aeronautical University,Daytona Beach,FL,32114","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074353293","display_name":"Lee M. Seversky","orcid":null},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lee Seversky","raw_affiliation_strings":["Air Force Research Laboratory,Rome,NY,13441"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Air Force Research Laboratory,Rome,NY,13441","institution_ids":["https://openalex.org/I1280414376"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102821804","display_name":"Chengtao Xu","orcid":"https://orcid.org/0000-0002-0067-3948"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengtao Xu","raw_affiliation_strings":["Embry-Riddle Aeronautical University,Daytona Beach,FL,32114"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embry-Riddle Aeronautical University,Daytona Beach,FL,32114","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101572181","display_name":"Dahai Liu","orcid":"https://orcid.org/0000-0003-4407-4047"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dahai Liu","raw_affiliation_strings":["Embry-Riddle Aeronautical University,Daytona Beach,FL,32114"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embry-Riddle Aeronautical University,Daytona Beach,FL,32114","institution_ids":["https://openalex.org/I84475105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079301418","display_name":"Houbing Song","orcid":"https://orcid.org/0000-0003-2631-9223"},"institutions":[{"id":"https://openalex.org/I84475105","display_name":"Embry\u2013Riddle Aeronautical University","ror":"https://ror.org/010jskt71","country_code":"US","type":"education","lineage":["https://openalex.org/I84475105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Houbing Song","raw_affiliation_strings":["Embry-Riddle Aeronautical University,Daytona Beach,FL,32114"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embry-Riddle Aeronautical University,Daytona Beach,FL,32114","institution_ids":["https://openalex.org/I84475105"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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":0.9991999864578247,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8178833723068237},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8147241473197937},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7806719541549683},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5280003547668457},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5207721590995789},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4956313371658325},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4767552316188812},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4683707654476166},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4586382806301117},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42254048585891724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4170871376991272},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39796629548072815},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09464079141616821}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8178833723068237},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8147241473197937},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7806719541549683},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5280003547668457},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5207721590995789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4956313371658325},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4767552316188812},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4683707654476166},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4586382806301117},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42254048585891724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4170871376991272},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39796629548072815},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09464079141616821}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ipccc51483.2021.9679367","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipccc51483.2021.9679367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","raw_type":"proceedings-article"},{"id":"pmh:oai:commons.erau.edu:publication-2777","is_oa":true,"landing_page_url":"https://commons.erau.edu/publication/1768","pdf_url":null,"source":{"id":"https://openalex.org/S4377196356","display_name":"Scholarly Commons (Embry\u2013Riddle Aeronautical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I84475105","host_organization_name":"Embry\u2013Riddle Aeronautical University","host_organization_lineage":["https://openalex.org/I84475105"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Publications","raw_type":"text"},{"id":"pmh:oai:works.bepress.com:dahailiu-1130","is_oa":false,"landing_page_url":"https://works.bepress.com/dahailiu/43","pdf_url":null,"source":{"id":"https://openalex.org/S4377196356","display_name":"Scholarly Commons (Embry\u2013Riddle Aeronautical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I84475105","host_organization_name":"Embry\u2013Riddle Aeronautical University","host_organization_lineage":["https://openalex.org/I84475105"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Dahai Liu","raw_type":"text"},{"id":"pmh:oai:works.bepress.com:houbing_song-1578","is_oa":false,"landing_page_url":"https://works.bepress.com/houbing_song/450","pdf_url":null,"source":{"id":"https://openalex.org/S4377196356","display_name":"Scholarly Commons (Embry\u2013Riddle Aeronautical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I84475105","host_organization_name":"Embry\u2013Riddle Aeronautical University","host_organization_lineage":["https://openalex.org/I84475105"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Houbing Song","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:commons.erau.edu:publication-2777","is_oa":true,"landing_page_url":"https://commons.erau.edu/publication/1768","pdf_url":null,"source":{"id":"https://openalex.org/S4377196356","display_name":"Scholarly Commons (Embry\u2013Riddle Aeronautical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I84475105","host_organization_name":"Embry\u2013Riddle Aeronautical University","host_organization_lineage":["https://openalex.org/I84475105"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Publications","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W592244745","https://openalex.org/W1485009520","https://openalex.org/W1924770834","https://openalex.org/W1959608418","https://openalex.org/W2064675550","https://openalex.org/W2086161653","https://openalex.org/W2116435618","https://openalex.org/W2296719434","https://openalex.org/W2541884796","https://openalex.org/W2768947629","https://openalex.org/W2785362611","https://openalex.org/W2786088545","https://openalex.org/W2786827964","https://openalex.org/W2807006176","https://openalex.org/W2911200746","https://openalex.org/W2912213068","https://openalex.org/W2948517885","https://openalex.org/W2949584022","https://openalex.org/W2950361482","https://openalex.org/W2951004968","https://openalex.org/W2955213239","https://openalex.org/W2962889061","https://openalex.org/W2963166639","https://openalex.org/W2975649990","https://openalex.org/W2982426954","https://openalex.org/W2994688391","https://openalex.org/W2995022099","https://openalex.org/W2995653155","https://openalex.org/W3015959599","https://openalex.org/W3016980352","https://openalex.org/W3081497074","https://openalex.org/W3098957257","https://openalex.org/W3105324058","https://openalex.org/W3105931142","https://openalex.org/W3106543020","https://openalex.org/W3128634608","https://openalex.org/W3151000187","https://openalex.org/W3169450514","https://openalex.org/W3175503219","https://openalex.org/W3184127157","https://openalex.org/W4294106961","https://openalex.org/W4318619660","https://openalex.org/W6617744952","https://openalex.org/W6628877408","https://openalex.org/W6640212811","https://openalex.org/W6640963894","https://openalex.org/W6677326919","https://openalex.org/W6728757088","https://openalex.org/W6745792373","https://openalex.org/W6748102297","https://openalex.org/W6752029299","https://openalex.org/W6765541894","https://openalex.org/W6776219759","https://openalex.org/W6785574322","https://openalex.org/W6793723616"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4220682630"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,52,98,176,206,219],"has":[2],"been":[3],"a":[4,44,101,113,144],"challenging":[5],"task":[6],"given":[7],"high-dimensional":[8],"multivariate":[9,166],"time":[10,33,95,167],"series":[11,96,168],"data":[12,39,41,62,137,169],"generated":[13],"by":[14],"networked":[15,182],"sensors":[16],"and":[17,28,47,73,115,161,173,210,218],"actuators":[18],"in":[19,43,75,100,164,213],"Cyber-Physical":[20],"Systems":[21],"(CPS).":[22],"Besides":[23],"the":[24,35,57,60,64,67,80,84,107,135,140,165,186,202],"highly":[25],"nonlinear,":[26],"complex,":[27],"dynamic":[29],"nature":[30],"of":[31,37,53,66,109,188,204,215],"such":[32],"series,":[34],"lack":[36],"labeled":[38],"impedes":[40],"exploitation":[42],"supervised":[45],"manner":[46],"thus":[48],"prevents":[49],"an":[50,93],"accurate":[51],"abnormal":[54,118],"phenomenons.":[55],"On":[56],"other":[58,192],"hand,":[59],"collected":[61],"at":[63,83,139],"edge":[65,141],"network":[68,114],"is":[69],"often":[70],"privacy":[71],"sensitive":[72],"large":[74],"quantity,":[76],"which":[77,157],"may":[78],"hinder":[79],"centralized":[81],"training":[82,136],"main":[85],"server.":[86],"To":[87,130],"tackle":[88],"these":[89],"issues,":[90],"we":[91,133],"propose":[92],"unsupervised":[94],"anomaly":[97,175],"framework":[99,207],"federated":[102,211],"fashion":[103],"to":[104,142,200],"continuously":[105],"monitor":[106],"behaviors":[108],"interconnected":[110],"devices":[111],"within":[112],"alert":[116],"for":[117,170],"incidents":[119],"so":[120],"that":[121],"countermeasures":[122],"can":[123],"be":[124,131],"taken":[125],"before":[126],"undesired":[127],"consequences":[128],"occur.":[129],"specific,":[132],"leave":[134],"distributed":[138],"learn":[143],"shared":[145],"Variational":[146],"Autoencoder":[147],"(VAE)":[148],"based":[149],"on":[150,179],"Convolutional":[151],"Gated":[152],"Recurrent":[153],"Unit":[154],"(ConvGRU)":[155],"model,":[156],"jointly":[158],"captures":[159],"feature":[160],"temporal":[162],"dependencies":[163],"representation":[171],"learning":[172],"downstream":[174],"tasks.":[177],"Experiments":[178],"three":[180],"real-world":[181],"sensor":[183],"datasets":[184],"illustrate":[185],"advantage":[187],"our":[189,205],"approach":[190],"over":[191],"state-of-the-art":[193],"models.":[194],"We":[195],"also":[196],"conduct":[197],"extensive":[198],"experiments":[199],"demonstrate":[201],"effectiveness":[203],"under":[208],"non-federated":[209],"settings":[212],"terms":[214],"overall":[216],"performance":[217],"latency.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
