{"id":"https://openalex.org/W3136022984","doi":"https://doi.org/10.1109/bigdata50022.2020.9378161","title":"Asynchronous Online Federated Learning for Edge Devices with Non-IID Data","display_name":"Asynchronous Online Federated Learning for Edge Devices with Non-IID Data","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3136022984","doi":"https://doi.org/10.1109/bigdata50022.2020.9378161","mag":"3136022984"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5101451077","display_name":"Yujing Chen","orcid":"https://orcid.org/0009-0000-6921-964X"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yujing Chen","raw_affiliation_strings":["Department of Computer Science, George Mason University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024383883","display_name":"Yue Ning","orcid":"https://orcid.org/0000-0002-1227-440X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Ning","raw_affiliation_strings":["Department of Computer Science, Stevens Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Stevens Institute of Technology","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055010301","display_name":"Martin Slawski","orcid":"https://orcid.org/0000-0003-0054-4764"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Slawski","raw_affiliation_strings":["Department of Statistics, George Mason University"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006581225","display_name":"Huzefa Rangwala","orcid":"https://orcid.org/0000-0003-0435-0035"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huzefa Rangwala","raw_affiliation_strings":["Department of Computer Science, George Mason University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101451077"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":28.2837,"has_fulltext":false,"cited_by_count":411,"citation_normalized_percentile":{"value":0.99720751,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9983999729156494,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.8541640639305115},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.8012303709983826},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.705141007900238},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6609460115432739},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5846607685089111},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5364333987236023},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4807857871055603},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4627893269062042},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.42244553565979004},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41032564640045166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31196045875549316},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30781036615371704},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.20171737670898438},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.10451894998550415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8541640639305115},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.8012303709983826},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.705141007900238},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6609460115432739},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5846607685089111},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5364333987236023},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4807857871055603},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4627893269062042},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.42244553565979004},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41032564640045166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31196045875549316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30781036615371704},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.20171737670898438},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.10451894998550415},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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":74,"referenced_works":["https://openalex.org/W1548525349","https://openalex.org/W1869943389","https://openalex.org/W1878852850","https://openalex.org/W1994348297","https://openalex.org/W2047894825","https://openalex.org/W2049599335","https://openalex.org/W2108475251","https://openalex.org/W2116772053","https://openalex.org/W2127941149","https://openalex.org/W2130062883","https://openalex.org/W2164278908","https://openalex.org/W2166268171","https://openalex.org/W2530417694","https://openalex.org/W2535838896","https://openalex.org/W2541884796","https://openalex.org/W2556660792","https://openalex.org/W2558150900","https://openalex.org/W2793925626","https://openalex.org/W2795423566","https://openalex.org/W2798720628","https://openalex.org/W2807006176","https://openalex.org/W2896422817","https://openalex.org/W2900120080","https://openalex.org/W2902113386","https://openalex.org/W2914328083","https://openalex.org/W2914622731","https://openalex.org/W2921434559","https://openalex.org/W2942571707","https://openalex.org/W2953498285","https://openalex.org/W2962696932","https://openalex.org/W2962741697","https://openalex.org/W2962788286","https://openalex.org/W2963191611","https://openalex.org/W2963247703","https://openalex.org/W2963300197","https://openalex.org/W2963403868","https://openalex.org/W2963433607","https://openalex.org/W2963773265","https://openalex.org/W2963804082","https://openalex.org/W2982676829","https://openalex.org/W3037871107","https://openalex.org/W3038022836","https://openalex.org/W3105122387","https://openalex.org/W3141595720","https://openalex.org/W4289147229","https://openalex.org/W4294106961","https://openalex.org/W4297687186","https://openalex.org/W4300427714","https://openalex.org/W4318619660","https://openalex.org/W4385245566","https://openalex.org/W6628887409","https://openalex.org/W6632849404","https://openalex.org/W6638803421","https://openalex.org/W6638900827","https://openalex.org/W6676540010","https://openalex.org/W6679154944","https://openalex.org/W6679314259","https://openalex.org/W6684379799","https://openalex.org/W6684472796","https://openalex.org/W6728757088","https://openalex.org/W6730236268","https://openalex.org/W6730493114","https://openalex.org/W6738383168","https://openalex.org/W6739901393","https://openalex.org/W6751961731","https://openalex.org/W6752029299","https://openalex.org/W6755988804","https://openalex.org/W6756756286","https://openalex.org/W6760214840","https://openalex.org/W6762519339","https://openalex.org/W6764791836","https://openalex.org/W6769202243","https://openalex.org/W6771536673","https://openalex.org/W6780226713"],"related_works":["https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W4313463218","https://openalex.org/W3106131444","https://openalex.org/W3216099748","https://openalex.org/W4205963435","https://openalex.org/W4312996489","https://openalex.org/W3214037210"],"abstract_inverted_index":{"Federated":[0,26,136],"learning":[1,6,146],"(FL)":[2],"is":[3,13,29],"a":[4,9,42,116,153,198],"machine":[5],"paradigm":[7],"where":[8,140],"shared":[10],"central":[11,154,165],"model":[12,157,166],"learned":[14],"across":[15],"distributed":[16],"devices":[17,83,112,123,143,184,187],"while":[18],"the":[19,30,46,55,60,69,81,125,141,164,173],"training":[20,35,70],"data":[21,66,151],"remains":[22],"on":[23,197],"these":[24],"devices.":[25,58,79],"Averaging":[27],"(FedAvg)":[28],"leading":[31],"optimization":[32],"method":[33],"for":[34],"non-convex":[36],"models":[37],"in":[38,68,87,167],"this":[39,129],"setting":[40],"with":[41,147,176,206],"synchronized":[43],"protocol.":[44],"However,":[45],"assumptions":[47],"made":[48],"by":[49],"FedAvg":[50],"are":[51],"not":[52],"realistic":[53],"given":[54],"heterogeneity":[56],"of":[57,64,77,118],"First,":[59],"volume":[61],"and":[62,89,97,152,185,202,216],"distribution":[63],"collected":[65],"vary":[67,86],"process":[71],"due":[72],"to":[73,102,115,124,171],"different":[74,104],"sampling":[75],"rates":[76],"edge":[78,82,111,122,142,183,186],"Second,":[80],"themselves":[84],"also":[85],"latency":[88],"system":[90],"configurations,":[91],"such":[92],"as":[93],"memory,":[94],"processor":[95],"speed,":[96],"power":[98],"requirements.":[99],"This":[100],"leads":[101],"vastly":[103],"computation":[105],"times.":[106],"Third,":[107],"availability":[108],"issues":[109],"at":[110,181],"can":[113],"lead":[114],"lack":[117],"contribution":[119],"from":[120,159],"specific":[121],"federated":[126],"model.":[127],"In":[128],"paper,":[130],"we":[131],"present":[132],"an":[133,168],"Asynchronous":[134],"Online":[135],"Learning":[137],"(ASO-Fed)":[138],"framework,":[139],"perform":[144,194],"online":[145],"continuous":[148],"streaming":[149,208],"local":[150],"server":[155],"aggregates":[156],"parameters":[158],"clients.":[160],"Our":[161],"framework":[162],"updates":[163],"asynchronous":[169],"manner":[170],"tackle":[172],"challenges":[174],"associated":[175],"both":[177],"varying":[178],"computational":[179],"loads":[180],"heterogeneous":[182],"that":[188],"lag":[189],"behind":[190],"or":[191],"dropout.":[192],"We":[193],"extensive":[195],"experiments":[196],"benchmark":[199],"image":[200],"dataset":[201],"three":[203],"real-world":[204],"datasets":[205],"non-IID":[207],"data.":[209],"The":[210],"results":[211],"demonstrate":[212],"ASO-Fed":[213],"converging":[214],"fast":[215],"maintaining":[217],"good":[218],"prediction":[219],"performance.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":81},{"year":2024,"cited_by_count":117},{"year":2023,"cited_by_count":94},{"year":2022,"cited_by_count":72},{"year":2021,"cited_by_count":35},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}