{"id":"https://openalex.org/W3200219902","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533808","title":"End-to-End Federated Learning for Autonomous Driving Vehicles","display_name":"End-to-End Federated Learning for Autonomous Driving Vehicles","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200219902","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533808","mag":"3200219902"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533808","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":"conference-paper","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/A5100659995","display_name":"Hongyi Zhang","orcid":"https://orcid.org/0000-0002-4132-6619"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Hongyi Zhang","raw_affiliation_strings":["Chalmers University of Technology, Gothenburg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010170972","display_name":"Jan Bosch","orcid":"https://orcid.org/0000-0003-2854-722X"},"institutions":[{"id":"https://openalex.org/I183111857","display_name":"Malm\u00f6 University","ror":"https://ror.org/05wp7an13","country_code":"SE","type":"education","lineage":["https://openalex.org/I183111857"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jan Bosch","raw_affiliation_strings":["Malm\u00f6 University, Malm\u00f6, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Malm\u00f6 University, Malm\u00f6, Sweden","institution_ids":["https://openalex.org/I183111857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049811300","display_name":"Helena Holmstr\u00f6m Olsson","orcid":"https://orcid.org/0000-0002-7700-1816"},"institutions":[{"id":"https://openalex.org/I183111857","display_name":"Malm\u00f6 University","ror":"https://ror.org/05wp7an13","country_code":"SE","type":"education","lineage":["https://openalex.org/I183111857"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Helena Holmstrom Olsson","raw_affiliation_strings":["Malm\u00f6 University, Malm\u00f6, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Malm\u00f6 University, Malm\u00f6, Sweden","institution_ids":["https://openalex.org/I183111857"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.1952,"has_fulltext":false,"cited_by_count":74,"citation_normalized_percentile":{"value":0.97839979,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","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"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9864000082015991,"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/computer-science","display_name":"Computer science","score":0.8433756828308105},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.7223656177520752},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6404085755348206},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.5243927836418152},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5048978924751282},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49051231145858765},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4512910842895508},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.45118317008018494},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43610766530036926},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.42998701333999634},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4036898910999298},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3690851926803589},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.16159602999687195},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.1251596212387085},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.10367193818092346}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8433756828308105},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.7223656177520752},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6404085755348206},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.5243927836418152},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5048978924751282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49051231145858765},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4512910842895508},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.45118317008018494},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43610766530036926},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42998701333999634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4036898910999298},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3690851926803589},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.16159602999687195},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.1251596212387085},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.10367193818092346},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533808","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"},{"id":"pmh:oai:research.chalmers.se:526573","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/526573","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1755205674","https://openalex.org/W2156303437","https://openalex.org/W2193413348","https://openalex.org/W2342840547","https://openalex.org/W2530417694","https://openalex.org/W2559767995","https://openalex.org/W2609731728","https://openalex.org/W2740067745","https://openalex.org/W2751023760","https://openalex.org/W2761595090","https://openalex.org/W2900120080","https://openalex.org/W2901064116","https://openalex.org/W2912213068","https://openalex.org/W2937443896","https://openalex.org/W2944264045","https://openalex.org/W2950290344","https://openalex.org/W2952087428","https://openalex.org/W2955213239","https://openalex.org/W2962804345","https://openalex.org/W2964121744","https://openalex.org/W2995653155","https://openalex.org/W2996058491","https://openalex.org/W3007607795","https://openalex.org/W3017201198","https://openalex.org/W3104353633","https://openalex.org/W3149731118","https://openalex.org/W4287903860","https://openalex.org/W4293580146","https://openalex.org/W6682864246","https://openalex.org/W6687566353","https://openalex.org/W6763907896"],"related_works":["https://openalex.org/W4313339048","https://openalex.org/W3201779876","https://openalex.org/W4386004629","https://openalex.org/W3176734149","https://openalex.org/W3113627641","https://openalex.org/W4238142035","https://openalex.org/W2885461866","https://openalex.org/W3191964704","https://openalex.org/W2901937988","https://openalex.org/W2942586735"],"abstract_inverted_index":{"In":[0,118],"recent":[1],"years,":[2],"with":[3,27,138],"the":[4,28,48,82,106,145,151,165,174,181,201],"development":[5],"of":[6,72,113,147,167],"computation":[7],"capability":[8],"in":[9,144],"devices,":[10],"companies":[11,32],"are":[12],"eager":[13],"to":[14,21,34,42,59,67,125,180,216],"investigate":[15],"and":[16,44,50,79,171,199],"utilize":[17],"suitable":[18],"ML/DL":[19,214],"methods":[20],"improve":[22,164],"their":[23],"service":[24],"quality.":[25],"However,":[26],"traditional":[29,182],"learning":[30],"strategy,":[31],"need":[33],"first":[35],"build":[36],"up":[37,110],"a":[38,111],"powerful":[39],"data":[40,46,89,103],"center":[41],"collect":[43],"analyze":[45],"from":[47,105],"edge":[49,107,169],"then":[51],"perform":[52],"centralized":[53,183],"model":[54,94,196],"training,":[55,81],"which":[56,204],"turns":[57],"out":[58],"be":[60],"inefficient.":[61],"Federated":[62,132,160,192],"Learning":[63,129,161,185,193],"has":[64,209],"been":[65],"introduced":[66],"solve":[68],"this":[69,119,207],"challenge.":[70],"Because":[71],"its":[73,188],"characteristics":[74],"such":[75],"as":[76,178],"model-only":[77],"exchange":[78],"parallel":[80],"technique":[83],"can":[84,99,162,194],"not":[85],"only":[86],"preserve":[87],"user":[88],"privacy":[90],"but":[91],"also":[92,172],"accelerate":[93,195],"training":[95,197],"speed.":[96],"The":[97],"method":[98],"easily":[100],"handle":[101],"real-time":[102],"generated":[104],"without":[108,187],"taking":[109],"lot":[112],"valuable":[114],"network":[115],"transmission":[116],"resources.":[117],"paper,":[120],"we":[121],"introduce":[122],"an":[123,139],"approach":[124,137,186,208],"end-to-end":[126],"on-device":[127],"Machine":[128,184],"by":[130],"utilizing":[131],"Learning.":[133],"We":[134],"validate":[135],"our":[136],"important":[140],"industrial":[141],"use":[142],"case":[143],"field":[146],"autonomous":[148],"driving":[149],"vehicles,":[150],"wheel":[152],"steering":[153],"angle":[154],"prediction.":[155],"Our":[156],"results":[157],"show":[158],"that":[159,206],"significantly":[163],"quality":[166],"local":[168],"models":[170],"reach":[173],"same":[175],"accuracy":[176],"level":[177],"compared":[179],"negative":[189],"effects.":[190],"Furthermore,":[191],"speed":[198],"reduce":[200],"communication":[202],"overhead,":[203],"proves":[205],"great":[210],"strength":[211],"when":[212],"deploying":[213],"components":[215],"various":[217],"real-world":[218],"embedded":[219],"systems.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}
