{"id":"https://openalex.org/W3200307514","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533419","title":"Anomaly Removal for Vehicle Energy Consumption in Federated Learning","display_name":"Anomaly Removal for Vehicle Energy Consumption in Federated Learning","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200307514","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533419","mag":"3200307514"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5067290556","display_name":"Guanyu Lin","orcid":"https://orcid.org/0000-0002-8459-4269"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guanyu Lin","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]},{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052848122","display_name":"Xinghua Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]},{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghua Zhu","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]},{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074472751","display_name":"Jianzong Wang","orcid":"https://orcid.org/0000-0002-9237-4231"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzong Wang","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]},{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016038454","display_name":"Jing Xiao","orcid":"https://orcid.org/0000-0001-9615-4749"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xiao","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]},{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067290556"],"corresponding_institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I4210152380","https://openalex.org/I4401726822","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0916,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.40938334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"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/T12095","display_name":"Vehicle emissions and performance","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.7905837297439575},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.726218581199646},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6070939302444458},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5835396647453308},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5767335891723633},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5104362368583679},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5011146068572998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4404391944408417},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.42264288663864136},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4199204444885254},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39210814237594604},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.39128464460372925},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.37688469886779785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3579133152961731},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2097204327583313},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1262819766998291}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7905837297439575},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.726218581199646},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6070939302444458},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5835396647453308},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5767335891723633},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5104362368583679},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5011146068572998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4404391944408417},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.42264288663864136},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4199204444885254},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39210814237594604},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.39128464460372925},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.37688469886779785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3579133152961731},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2097204327583313},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1262819766998291},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G3694455423","display_name":null,"funder_award_id":"2017YFB1401202,2018YFB0204403,2018YFB1003500","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":36,"referenced_works":["https://openalex.org/W128424097","https://openalex.org/W187043655","https://openalex.org/W201138236","https://openalex.org/W2000661457","https://openalex.org/W2116435618","https://openalex.org/W2122538988","https://openalex.org/W2166448956","https://openalex.org/W2466206609","https://openalex.org/W2541884796","https://openalex.org/W2551621012","https://openalex.org/W2589456662","https://openalex.org/W2768181417","https://openalex.org/W2790864385","https://openalex.org/W2796882046","https://openalex.org/W2905737086","https://openalex.org/W2928675906","https://openalex.org/W2932010921","https://openalex.org/W2943795187","https://openalex.org/W2966559104","https://openalex.org/W3001299093","https://openalex.org/W3010852232","https://openalex.org/W3033986201","https://openalex.org/W3041934925","https://openalex.org/W3042621011","https://openalex.org/W3097982226","https://openalex.org/W3101220048","https://openalex.org/W3111921477","https://openalex.org/W4286411816","https://openalex.org/W4318619660","https://openalex.org/W6605215819","https://openalex.org/W6608319088","https://openalex.org/W6677326919","https://openalex.org/W6728757088","https://openalex.org/W6762205623","https://openalex.org/W6778663327","https://openalex.org/W6787660307"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W4285233543","https://openalex.org/W4230838436","https://openalex.org/W4214747436","https://openalex.org/W4399531511"],"abstract_inverted_index":{"Federated":[0],"learning":[1,6,13,55,131],"is":[2,31,73],"a":[3,97],"distributed":[4,12,98,111],"deep":[5],"method":[7],"that":[8,83,154],"enables":[9],"parallel":[10],"and":[11,80,124,142,162,170],"of":[14,65,89,136,146,164],"data":[15,37,44,59,138],"on":[16,115,129,139,148],"multiple":[17],"participants,":[18],"without":[19,175],"the":[20,35,39,66,86,90,105,134,144,156,165],"need":[21],"to":[22,33,43,56,75,109,132],"centrally":[23],"store":[24],"it.":[25],"In":[26,100],"intelligent":[27],"transportation":[28],"system,":[29],"it":[30],"impractical":[32],"gather":[34],"vehicle":[36,58,112,140],"from":[38],"edge":[40],"devices":[41],"due":[42],"privacy":[45],"concerns":[46],"or":[47],"network":[48,92,126,167],"bandwidth":[49],"limitation.":[50],"Hence,":[51],"combining":[52],"with":[53,155],"federated":[54,130],"train":[57],"processing":[60],"models":[61],"has":[62],"become":[63],"one":[64],"popular":[67],"solutions.":[68],"However,":[69],"such":[70],"computing":[71,87],"paradigm":[72],"subject":[74],"threats":[76],"posed":[77],"by":[78],"malicious":[79],"abnormal":[81],"nodes":[82],"greatly":[84],"reduces":[85],"power":[88],"neural":[91,125],"when":[93],"performing":[94],"calculations":[95],"in":[96],"manner.":[99],"this":[101],"paper,":[102],"we":[103,118],"use":[104],"Vehicle":[106],"Energy":[107],"Dataset":[108],"simulate":[110],"data.":[113],"Based":[114],"these":[116],"data,":[117],"propose":[119],"an":[120],"unsupervised":[121],"anomaly":[122,158,176],"removal":[123,159],"model":[127],"based":[128],"solve":[133],"problem":[135],"outlier":[137],"equipment":[141],"analyze":[143],"effect":[145],"speed":[147],"fuel":[149],"consumption.":[150],"The":[151],"results":[152],"show":[153],"proposed":[157],"strategy,":[160],"MAE":[161],"MSE":[163],"trained":[166],"are":[168],"29%":[169],"36%":[171],"lower":[172],"than":[173],"those":[174],"removal,":[177],"respectively.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}