{"id":"https://openalex.org/W4392248729","doi":"https://doi.org/10.1109/icce59016.2024.10444369","title":"ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights","display_name":"ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights","publication_year":2024,"publication_date":"2024-01-06","ids":{"openalex":"https://openalex.org/W4392248729","doi":"https://doi.org/10.1109/icce59016.2024.10444369"},"language":"en","primary_location":{"id":"doi:10.1109/icce59016.2024.10444369","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icce59016.2024.10444369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","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/A5040547644","display_name":"Atah Nuh Mih","orcid":null},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Atah Nuh Mih","raw_affiliation_strings":["University of New Brunswick,Analytics Everywhere Lab,Canada","Analytics Everywhere Lab, University of New Brunswick, Canada"],"affiliations":[{"raw_affiliation_string":"University of New Brunswick,Analytics Everywhere Lab,Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"Analytics Everywhere Lab, University of New Brunswick, Canada","institution_ids":["https://openalex.org/I106938459"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088383217","display_name":"Hung Cao","orcid":"https://orcid.org/0000-0002-0788-4377"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hung Cao","raw_affiliation_strings":["University of New Brunswick,Analytics Everywhere Lab,Canada","Analytics Everywhere Lab, University of New Brunswick, Canada"],"affiliations":[{"raw_affiliation_string":"University of New Brunswick,Analytics Everywhere Lab,Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"Analytics Everywhere Lab, University of New Brunswick, Canada","institution_ids":["https://openalex.org/I106938459"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093367484","display_name":"Asfia Kawnine","orcid":null},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Asfia Kawnine","raw_affiliation_strings":["University of New Brunswick,Analytics Everywhere Lab,Canada","Analytics Everywhere Lab, University of New Brunswick, Canada"],"affiliations":[{"raw_affiliation_string":"University of New Brunswick,Analytics Everywhere Lab,Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"Analytics Everywhere Lab, University of New Brunswick, Canada","institution_ids":["https://openalex.org/I106938459"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068240080","display_name":"M\u00f3nica Wachowicz","orcid":"https://orcid.org/0000-0002-4659-0101"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]},{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU","CA"],"is_corresponding":false,"raw_author_name":"Monica Wachowicz","raw_affiliation_strings":["University of New Brunswick,Analytics Everywhere Lab,Canada","Analytics Everywhere Lab, University of New Brunswick, Canada","RMIT University, Australia"],"affiliations":[{"raw_affiliation_string":"University of New Brunswick,Analytics Everywhere Lab,Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"Analytics Everywhere Lab, University of New Brunswick, Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"RMIT University, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040547644"],"corresponding_institution_ids":["https://openalex.org/I106938459"],"apc_list":null,"apc_paid":null,"fwci":0.3506,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54694578,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9947999715805054,"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"}},"topics":[{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9947999715805054,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9933000206947327,"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":0.9758999943733215,"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/cloud-computing","display_name":"Cloud computing","score":0.7689608335494995},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6621573567390442},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6027970910072327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28246498107910156},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.06843462586402893}],"concepts":[{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7689608335494995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621573567390442},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6027970910072327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28246498107910156},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.06843462586402893}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce59016.2024.10444369","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icce59016.2024.10444369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334111","display_name":"Innovation Fund","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1603035390","https://openalex.org/W2030290736","https://openalex.org/W2962814013","https://openalex.org/W2963163009","https://openalex.org/W2963318081","https://openalex.org/W2964787841","https://openalex.org/W2997878718","https://openalex.org/W3009857025","https://openalex.org/W3018757597","https://openalex.org/W3026465556","https://openalex.org/W3095611529","https://openalex.org/W3098486933","https://openalex.org/W3118608800","https://openalex.org/W3166166117","https://openalex.org/W3181552969","https://openalex.org/W3204369957","https://openalex.org/W4285217780","https://openalex.org/W4307950645","https://openalex.org/W4313555467","https://openalex.org/W4387746069","https://openalex.org/W6762718338","https://openalex.org/W6777046832","https://openalex.org/W6780052409","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4244478748","https://openalex.org/W4223488648","https://openalex.org/W2134969820","https://openalex.org/W2251605416","https://openalex.org/W1997222214","https://openalex.org/W2560439919","https://openalex.org/W4389340727"],"abstract_inverted_index":{"The":[0,98,120],"use":[1],"of":[2,15,22,130,244],"edge":[3,26,47,80,118,188,214],"devices":[4,16,27,48,81],"together":[5],"with":[6,127,157,183],"cloud":[7],"provides":[8],"a":[9,72,105,137,143,154,158,191,205],"collaborative":[10,73],"relationship":[11],"between":[12],"both":[13,210],"classes":[14],"where":[17,176],"one":[18],"complements":[19],"the":[20,23,31,44,55,65,91,94,101,112,116,128,131,167,180,187,198,211,222],"shortcomings":[21],"other.":[24],"Resource-constraint":[25],"can":[28,49],"benefit":[29],"from":[30,115],"abundant":[32],"computing":[33],"power":[34],"provided":[35],"by":[36,38],"servers":[37],"offloading":[39],"computationally":[40,61],"intensive":[41,62],"tasks":[42,63,172],"to":[43,54,58,93,217],"server.":[45],"Meanwhile,":[46],"leverage":[50],"their":[51,86],"close":[52],"proximity":[53],"data":[56,114],"source":[57],"perform":[59],"less":[60],"on":[64,85,111,179,209,234],"data.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70,225],"propose":[71],"edge-cloud":[74],"paradigm":[75],"called":[76],"ECAvg":[77],"in":[78,166,204,207],"which":[79,108],"pre-train":[82],"local":[83,121],"models":[84,92,123,189,215],"respective":[87],"datasets":[88],"and":[89,149,169,186,213,241],"transfer":[90,219],"server":[95,99,181,212],"for":[96],"fine-tuning.":[97],"averages":[100],"pre-trained":[102],"weights":[103,129,185,202],"into":[104],"global":[106,132],"model,":[107],"is":[109,230],"fine-tuned":[110],"combined":[113],"various":[117],"devices.":[119],"(edge)":[122],"are":[124],"then":[125],"updated":[126],"(server)":[133],"model.":[134],"We":[135,162],"implement":[136],"CIFAR-10":[138,168],"classification":[139,145,152,171],"task":[140,146],"using":[141,147,153,173],"MobileNetV2,":[142],"CIFAR-100":[144,170],"ResNet50,":[148],"an":[150],"MNIST":[151,199],"neural":[155,236,246],"network":[156],"single":[159],"hidden":[160],"layer.":[161],"observed":[163],"performance":[164,177,193,208],"improvement":[165],"our":[174,228],"approach,":[175],"improved":[178],"model":[182,195],"averaged":[184],"had":[190],"better":[192],"after":[194],"update.":[196],"On":[197],"classification,":[200],"averaging":[201],"resulted":[203],"drop":[206],"due":[216],"negative":[218],"learning.":[220],"From":[221],"experiment":[223],"results,":[224],"conclude":[226],"that":[227],"approach":[229],"successful":[231],"when":[232],"implemented":[233],"deep":[235],"networks":[237],"such":[238],"as":[239],"MobileNetV2":[240],"ResNet50":[242],"instead":[243],"simple":[245],"networks.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}