{"id":"https://openalex.org/W4241905619","doi":"https://doi.org/10.1109/iccad.2017.8203791","title":"AdaLearner: An adaptive distributed mobile learning system for neural networks","display_name":"AdaLearner: An adaptive distributed mobile learning system for neural networks","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W4241905619","doi":"https://doi.org/10.1109/iccad.2017.8203791"},"language":"en","primary_location":{"id":"doi:10.1109/iccad.2017.8203791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad.2017.8203791","pdf_url":null,"source":{"id":"https://openalex.org/S4363608376","display_name":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","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/A5102935726","display_name":"Jiachen Mao","orcid":"https://orcid.org/0000-0001-8986-0696"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiachen Mao","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018832267","display_name":"Zhuwei Qin","orcid":"https://orcid.org/0000-0002-5465-7740"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuwei Qin","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030027584","display_name":"Zirui Xu","orcid":"https://orcid.org/0000-0002-3556-9358"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zirui Xu","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090657983","display_name":"Kent W. Nixon","orcid":"https://orcid.org/0000-0001-6764-8782"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kent W. Nixon","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441949","display_name":"Xiang Chen","orcid":"https://orcid.org/0000-0002-9800-6472"},"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":"Xiang Chen","raw_affiliation_strings":["George Mason University, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100429403","display_name":"Hai Li","orcid":"https://orcid.org/0000-0003-3228-6544"},"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":"Hai Li","raw_affiliation_strings":["George Mason University, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"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":"Yiran Chen","raw_affiliation_strings":["George Mason University, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5102935726"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.4925,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.79072464,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"291","last_page":"296"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9980999827384949,"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/T12676","display_name":"Machine Learning and ELM","score":0.9977999925613403,"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/computer-science","display_name":"Computer science","score":0.8620551824569702},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.750219464302063},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6498844027519226},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6361522674560547},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.601806104183197},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5916836261749268},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5872271656990051},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5728501677513123},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5574474930763245},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4985239505767822},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4565240740776062},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43335258960723877},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3419416546821594},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.28080588579177856},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.1695978343486786},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.12139159440994263}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8620551824569702},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.750219464302063},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6498844027519226},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6361522674560547},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.601806104183197},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5916836261749268},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5872271656990051},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5728501677513123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5574474930763245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4985239505767822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4565240740776062},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43335258960723877},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3419416546821594},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.28080588579177856},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.1695978343486786},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.12139159440994263},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad.2017.8203791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad.2017.8203791","pdf_url":null,"source":{"id":"https://openalex.org/S4363608376","display_name":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1825216778","https://openalex.org/W2060393849","https://openalex.org/W2107438106","https://openalex.org/W2112796928","https://openalex.org/W2121969814","https://openalex.org/W2145339207","https://openalex.org/W2163605009","https://openalex.org/W2168231600","https://openalex.org/W2177838837","https://openalex.org/W2186615578","https://openalex.org/W2271840356","https://openalex.org/W2276486856","https://openalex.org/W2407022425","https://openalex.org/W2560217098","https://openalex.org/W2612193523","https://openalex.org/W2613989746","https://openalex.org/W2963004077","https://openalex.org/W3118608800","https://openalex.org/W6665801690","https://openalex.org/W6676105031","https://openalex.org/W6678509853","https://openalex.org/W6684191040","https://openalex.org/W6684859321","https://openalex.org/W6685682220"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W17155033","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W1485337887","https://openalex.org/W1966837078"],"abstract_inverted_index":{"Neural":[0],"networks":[1,63,90,117],"hold":[2],"a":[3,93],"critical":[4],"domain":[5],"in":[6,22,105],"machine":[7],"learning":[8,86],"algorithms":[9],"because":[10],"of":[11,111],"their":[12],"self-adaptiveness":[13],"and":[14,39,124,176],"state-of-the-art":[15],"performance.":[16],"Before":[17],"the":[18,48,101,109,127,148,162,171],"testing":[19],"(inference)":[20],"phases":[21,28],"practical":[23],"use,":[24],"sophisticated":[25],"training":[26,34,49,61,118,149],"(learning)":[27],"are":[29],"required,":[30],"calling":[31],"for":[32,88,130],"efficient":[33],"methods":[35],"with":[36,96],"higher":[37],"accuracy":[38],"shorter":[40],"converging":[41],"time.":[42],"Many":[43],"existing":[44],"studies":[45],"focus":[46],"on":[47,51,64,154],"optimization":[50],"high-performance":[52],"servers":[53],"or":[54],"computing":[55],"clusters,":[56],"e.g.":[57,67],"GPU":[58],"clusters.":[59],"However,":[60],"neural":[62,89,116,137],"resource-constrained":[65],"devices,":[66],"mobile":[68,85,98,121],"platforms,":[69],"is":[70,158],"an":[71],"important":[72],"research":[73],"topic":[74],"barely":[75],"touched.":[76],"In":[77],"this":[78],"paper,":[79],"we":[80,114],"implement":[81],"AdaLearner-an":[82],"adaptive":[83],"distributed":[84],"system":[87,132],"that":[91],"trains":[92],"single":[94],"network":[95,104,138],"heterogenous":[97],"resources":[99,123],"under":[100],"same":[102],"local":[103],"parallel.":[106],"To":[107],"exploit":[108],"potential":[110],"our":[112],"system,":[113],"adapt":[115],"phase":[119,150],"to":[120,167,170],"device-wise":[122],"fiercely":[125],"decrease":[126],"transmission":[128],"overhead":[129],"better":[131],"scalability.":[133,178],"On":[134],"three":[135],"representative":[136],"structures":[139],"trained":[140],"from":[141,165],"two":[142],"image":[143],"classification":[144],"datasets,":[145],"AdaLearner":[146],"boosts":[147],"significantly.":[151],"For":[152],"example,":[153],"LeNet,":[155],"1.75-3.37\u00d7":[156],"speedup":[157],"achieved":[159,172],"when":[160],"increasing":[161],"worker":[163],"nodes":[164],"2":[166],"8,":[168],"thanks":[169],"high":[173],"execution":[174],"parallelism":[175],"excellent":[177]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
