{"id":"https://openalex.org/W4306952702","doi":"https://doi.org/10.1145/3538641.3561500","title":"An efficient split learning framework for recurrent neural network in mobile edge environment","display_name":"An efficient split learning framework for recurrent neural network in mobile edge environment","publication_year":2022,"publication_date":"2022-10-03","ids":{"openalex":"https://openalex.org/W4306952702","doi":"https://doi.org/10.1145/3538641.3561500"},"language":"en","primary_location":{"id":"doi:10.1145/3538641.3561500","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3538641.3561500","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Conference on Research in Adaptive and Convergent Systems","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/A5080937886","display_name":"Wenxuan Zhou","orcid":"https://orcid.org/0000-0003-1199-885X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxuan Zhou","raw_affiliation_strings":["Hohai University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017137055","display_name":"Zhihao Qu","orcid":"https://orcid.org/0000-0001-7538-1985"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihao Qu","raw_affiliation_strings":["Hohai University, Nanjing, Jiangsu, China and Nanjing University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University, Nanjing, Jiangsu, China and Nanjing University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I163340411","https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017359136","display_name":"Yanchao Zhao","orcid":"https://orcid.org/0000-0001-6314-0332"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanchao Zhao","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000882226","display_name":"Bin Tang","orcid":"https://orcid.org/0000-0002-4577-8882"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Tang","raw_affiliation_strings":["Hohai University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087746903","display_name":"Baoliu Ye","orcid":"https://orcid.org/0000-0003-1065-449X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoliu Ye","raw_affiliation_strings":["Nanjing University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"131","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9987999796867371,"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":0.9987999796867371,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9958999752998352,"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/T12676","display_name":"Machine Learning and ELM","score":0.9937999844551086,"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":0.8500674962997437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6180897951126099},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.615982711315155},{"id":"https://openalex.org/keywords/mobile-edge-computing","display_name":"Mobile edge computing","score":0.5889145135879517},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5865411162376404},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5565149188041687},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5440946221351624},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5009794235229492},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.46315068006515503},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4245753288269043},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.42269521951675415},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.10604515671730042}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8500674962997437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6180897951126099},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.615982711315155},{"id":"https://openalex.org/C2776061582","wikidata":"https://www.wikidata.org/wiki/Q25325231","display_name":"Mobile edge computing","level":3,"score":0.5889145135879517},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5865411162376404},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5565149188041687},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5440946221351624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5009794235229492},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.46315068006515503},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4245753288269043},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.42269521951675415},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.10604515671730042},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3538641.3561500","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3538641.3561500","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Conference on Research in Adaptive and Convergent Systems","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":7,"referenced_works":["https://openalex.org/W2144499799","https://openalex.org/W2194775991","https://openalex.org/W2963209930","https://openalex.org/W3004469560","https://openalex.org/W3091884625","https://openalex.org/W3213008925","https://openalex.org/W3213120752"],"related_works":["https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W4225394202","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560","https://openalex.org/W2969450769"],"abstract_inverted_index":{"Split":[0],"learning":[1,7,28,60],"is":[2,73],"an":[3,108,114],"emerging":[4],"distributed":[5],"machine":[6],"paradigm":[8],"that":[9,111],"exhibits":[10],"great":[11],"advantages":[12],"in":[13,52,64],"privacy":[14],"protection":[15],"and":[16,34,100,124,131],"training":[17,98],"efficiency.":[18],"Most":[19],"previous":[20],"studies":[21],"pay":[22],"attention":[23],"to":[24,74,80],"client-server":[25],"based":[26],"split":[27,48,59],"on":[29,40,121],"Convolutional":[30],"Neural":[31,44],"networks":[32],"(CNNs),":[33],"little":[35],"effort":[36],"has":[37,113],"been":[38],"spent":[39],"investigating":[41],"the":[42,88,92,96,129,132,135],"Recurrent":[43],"Networks":[45],"(RNNs)":[46],"oriented":[47],"learning.":[49],"To":[50],"fill":[51],"this":[53],"gap,":[54],"we":[55,106],"propose":[56,107],"a":[57,83],"multi-hop":[58],"algorithm":[61,110],"for":[62],"RNN":[63],"mobile":[65,78],"edge":[66],"computing,":[67],"namely":[68],"M-SLRNN.":[69],"The":[70],"core":[71],"idea":[72],"efficiently":[75],"orchestrate":[76],"resource-constrained":[77],"devices":[79],"collaboratively":[81],"train":[82],"large-size":[84],"model.":[85],"We":[86],"formulate":[87],"optimization":[89],"problem":[90],"with":[91],"objective":[93],"of":[94,117,134],"minimizing":[95],"total":[97],"time":[99],"theoretically":[101],"analyze":[102],"its":[103],"hardness.":[104],"Then,":[105],"efficient":[109],"conditionally":[112],"approximation":[115],"ratio":[116],"2.":[118],"Extensive":[119],"experiments":[120],"both":[122],"image":[123],"text":[125],"classification":[126],"tasks":[127],"demonstrate":[128],"effectiveness":[130],"efficiency":[133],"proposed":[136],"algorithm.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}