{"id":"https://openalex.org/W3208284577","doi":"https://doi.org/10.1145/3459637.3482412","title":"ALADDIN","display_name":"ALADDIN","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3208284577","doi":"https://doi.org/10.1145/3459637.3482412","mag":"3208284577"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482412","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","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/A5042828356","display_name":"Yunyong Ko","orcid":"https://orcid.org/0000-0003-1283-4697"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yunyong Ko","raw_affiliation_strings":["Hanyang University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Hanyang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081686810","display_name":"Kibong Choi","orcid":"https://orcid.org/0000-0002-2798-3698"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kibong Choi","raw_affiliation_strings":["Hanyang University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Hanyang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089169293","display_name":"Hyunseung Jei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hyunseung Jei","raw_affiliation_strings":["SK Telecom, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"SK Telecom, Seoul, Republic of Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405086","display_name":"Dongwon Lee","orcid":"https://orcid.org/0000-0001-8371-7629"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongwon Lee","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100656150","display_name":"Sang\u2010Wook Kim","orcid":"https://orcid.org/0000-0002-6345-9084"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Wook Kim","raw_affiliation_strings":["Hanyang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Hanyang University, Seoul, South Korea","institution_ids":["https://openalex.org/I4575257"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042828356"],"corresponding_institution_ids":["https://openalex.org/I4575257"],"apc_list":null,"apc_paid":null,"fwci":0.6798,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76161721,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"863","last_page":"872"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9997000098228455,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9997000098228455,"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/T13553","display_name":"Age of Information Optimization","score":0.9980999827384949,"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":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.8683218955993652},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7487142086029053},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.7375459671020508},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6267122030258179},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5858114957809448},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5810001492500305},{"id":"https://openalex.org/keywords/dilemma","display_name":"Dilemma","score":0.568363606929779},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5533561110496521},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.4984264373779297},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.37768054008483887},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3676967918872833},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35627931356430054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32436603307724},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.20365944504737854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15458399057388306},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13124465942382812}],"concepts":[{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.8683218955993652},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487142086029053},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.7375459671020508},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6267122030258179},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5858114957809448},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5810001492500305},{"id":"https://openalex.org/C2778496695","wikidata":"https://www.wikidata.org/wiki/Q254128","display_name":"Dilemma","level":2,"score":0.568363606929779},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5533561110496521},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.4984264373779297},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37768054008483887},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3676967918872833},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35627931356430054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32436603307724},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.20365944504737854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15458399057388306},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13124465942382812},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482412","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G2155016845","display_name":null,"funder_award_id":"#212114824","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4526159756","display_name":null,"funder_award_id":"NRF-2020R1A2B5B03001960","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1442374986","https://openalex.org/W1975934607","https://openalex.org/W2018047324","https://openalex.org/W2060393849","https://openalex.org/W2083842231","https://openalex.org/W2110100895","https://openalex.org/W2117539524","https://openalex.org/W2132737349","https://openalex.org/W2168231600","https://openalex.org/W2194775991","https://openalex.org/W2558946687","https://openalex.org/W2612026221","https://openalex.org/W2626721309","https://openalex.org/W2803937730","https://openalex.org/W2890397435","https://openalex.org/W2897021175","https://openalex.org/W2902410171","https://openalex.org/W2904556356","https://openalex.org/W2951781666","https://openalex.org/W2963228337","https://openalex.org/W2963804082","https://openalex.org/W2969388332","https://openalex.org/W2982664135","https://openalex.org/W2988070836","https://openalex.org/W2991040477","https://openalex.org/W3003571307","https://openalex.org/W3023944502","https://openalex.org/W3041516094","https://openalex.org/W3114060070","https://openalex.org/W3118608800","https://openalex.org/W3174633912"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W2135248929","https://openalex.org/W2092690310","https://openalex.org/W1493624592","https://openalex.org/W3190662310","https://openalex.org/W2798440551","https://openalex.org/W1507878993"],"abstract_inverted_index":{"To":[0],"speed":[1],"up":[2,154],"the":[3,30,41,81,97,108,118,124,131,166,178,184,188,193],"training":[4,13,46,70,134,151],"of":[5,25,111,126,133,183,195],"massive":[6],"deep":[7],"neural":[8],"network":[9],"(DNN)":[10],"models,":[11],"distributed":[12,26],"has":[14],"been":[15],"widely":[16],"studied.":[17],"In":[18],"general,":[19],"a":[20,23,34,44,67,102,162],"centralized":[21,69],"training,":[22,27],"type":[24],"suffers":[28,47],"from":[29,48],"communication":[31,75],"bottleneck":[32,83],"between":[33,76],"parameter":[35,50],"server":[36],"(PS)":[37],"and":[38,78,85,92,128,141,157,165,191],"workers.":[39],"On":[40],"other":[42],"hand,":[43],"decentralized":[45,168],"increased":[49,98],"variance":[51,99],"among":[52],"workers":[53,79,127],"that":[54,107,145],"causes":[55],"slower":[56],"model":[57],"convergence.":[58],"Addressing":[59],"this":[60,63],"dilemma,":[61],"in":[62],"work,":[64],"we":[65,105],"propose":[66],"novel":[68,86],"algorithm,":[71,169,186],"ALADDIN,":[72],"employing":[73],"\"asymmetric\"":[74],"PS":[77,82],"for":[80,89],"problem":[84],"updating":[87],"strategies":[88],"both":[90],"local":[91],"global":[93],"parameters":[94],"to":[95,155,161,177,181],"mitigate":[96],"problem.":[100],"Through":[101],"convergence":[103,109,194],"analysis,":[104],"show":[106],"rate":[110],"ALADDIN":[112,147,175,196],"is":[113,123,130,197],"O(1":[114],"\u00f8nk":[115],")":[116],"on":[117],"non-convex":[119],"problem,":[120],"where":[121],"n":[122],"number":[125,132],"k":[129],"iterations.":[135],"The":[136],"empirical":[137],"evaluation":[138],"using":[139],"ResNet-50":[140],"VGG-16":[142],"models":[143,172],"demonstrates":[144],"(1)":[146],"shows":[148],"significantly":[149],"better":[150],"throughput":[152],"with":[153],"191%":[156],"34%":[158],"improvement":[159],"compared":[160],"synchronous":[163,185],"algorithm":[164],"state-of-the-art":[167],"respectively,":[170],"(2)":[171],"trained":[173],"by":[174],"converge":[176],"accuracies,":[179],"comparable":[180],"those":[182],"within":[187],"shortest":[189],"time,":[190],"(3)":[192],"robust":[198],"under":[199],"various":[200],"heterogeneous":[201],"environments.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-11-08T00:00:00"}
