{"id":"https://openalex.org/W4296079296","doi":"https://doi.org/10.1109/tvt.2022.3206876","title":"Partially Collaborative Edge Caching Based on Federated Deep Reinforcement Learning","display_name":"Partially Collaborative Edge Caching Based on Federated Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-09-16","ids":{"openalex":"https://openalex.org/W4296079296","doi":"https://doi.org/10.1109/tvt.2022.3206876"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2022.3206876","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2022.3206876","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-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/A5112272361","display_name":"Meng Lei","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Lei","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430045","display_name":"Qiang Li","orcid":"https://orcid.org/0000-0003-1471-3821"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Li","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-1471-3821","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057308968","display_name":"Xiaohu Ge","orcid":"https://orcid.org/0000-0002-3204-5241"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohu Ge","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039977183","display_name":"Ashish Pandharipande","orcid":"https://orcid.org/0000-0002-7920-3964"},"institutions":[{"id":"https://openalex.org/I109147379","display_name":"NXP (Netherlands)","ror":"https://ror.org/059be4e97","country_code":"NL","type":"company","lineage":["https://openalex.org/I109147379"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ashish Pandharipande","raw_affiliation_strings":["NXP Semiconductors, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NXP Semiconductors, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I109147379"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8704,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.85999821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"72","issue":"1","first_page":"1389","last_page":"1394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9998999834060669,"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/T11478","display_name":"Caching and Content Delivery","score":0.9998999834060669,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10796","display_name":"Cooperative Communication and Network Coding","score":0.9866999983787537,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.8338698744773865},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8181948065757751},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.80014967918396},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6182054877281189},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5308049321174622},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5231431722640991},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.5015106201171875},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.4837891757488251},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.45386433601379395},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.45302292704582214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3080247640609741},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.15813854336738586}],"concepts":[{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.8338698744773865},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8181948065757751},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.80014967918396},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6182054877281189},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5308049321174622},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5231431722640991},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.5015106201171875},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.4837891757488251},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.45386433601379395},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.45302292704582214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3080247640609741},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.15813854336738586},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2022.3206876","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2022.3206876","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.550000011920929,"display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G861332501","display_name":"\u9762\u5411\u89e6\u89c9\u4e92\u8054\u7f51\u7684\u8fb9\u7f18\u8ba1\u7b97\u3001\u7f13\u5b58\u3001\u901a\u4fe1\u4e0e\u63a7\u5236\u878d\u5408\u7814\u7a76","funder_award_id":"61971461","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1993484402","https://openalex.org/W2219888463","https://openalex.org/W2425152312","https://openalex.org/W2756144809","https://openalex.org/W2764329469","https://openalex.org/W2898754723","https://openalex.org/W2917116807","https://openalex.org/W2969218621","https://openalex.org/W2990993261","https://openalex.org/W2999263473","https://openalex.org/W3006403513","https://openalex.org/W3006655855","https://openalex.org/W3015636663","https://openalex.org/W3016207298","https://openalex.org/W3033403733","https://openalex.org/W3034677867","https://openalex.org/W3046032603","https://openalex.org/W3049535205","https://openalex.org/W3088084782","https://openalex.org/W3090944066","https://openalex.org/W3101718285","https://openalex.org/W3113024443","https://openalex.org/W3122800475","https://openalex.org/W3157850870","https://openalex.org/W3162171002","https://openalex.org/W3172770020","https://openalex.org/W3184838508","https://openalex.org/W3196463200","https://openalex.org/W3212948368","https://openalex.org/W6780319995"],"related_works":["https://openalex.org/W2092530219","https://openalex.org/W2388464034","https://openalex.org/W2533125852","https://openalex.org/W2140460949","https://openalex.org/W2105580438","https://openalex.org/W4298221930","https://openalex.org/W2057435755","https://openalex.org/W2777914285","https://openalex.org/W4306904969","https://openalex.org/W4320067866"],"abstract_inverted_index":{"In":[0,17],"this":[1,84],"paper,":[2],"edge":[3],"caching":[4,43,89],"is":[5,32,92],"investigated":[6],"in":[7,34,138],"fog":[8,37],"radio":[9],"access":[10,38],"networks":[11],"subject":[12],"to":[13,19,54,74,108],"dynamic":[14],"content":[15],"popularity.":[16],"order":[18],"improve":[20],"the":[21,46,55,62,80,96,113,135,148],"long-term":[22],"cache-hit-ratio":[23],"(CHR),":[24],"a":[25,49,76,86,110,124],"federated":[26,64],"deep":[27],"reinforcement":[28],"learning":[29,65],"(FDRL)":[30],"framework":[31],"proposed,":[33],"which":[35],"multiple":[36],"points":[39],"(FAPs)":[40],"adjust":[41],"their":[42],"strategies":[44],"under":[45],"coordination":[47],"of":[48,140],"central":[50],"server":[51],"(CS).":[52],"Owing":[53],"non-i.i.d":[56],"data":[57,97],"acquired":[58],"over":[59],"different":[60],"FAPs,":[61],"traditional":[63],"(FL)":[66],"method":[67],"may":[68],"suffer":[69],"from":[70],"performance":[71,130],"loss":[72],"due":[73],"maintaining":[75],"global":[77,120],"model":[78],"at":[79,105],"CS.":[81],"To":[82],"address":[83],"shortcoming,":[85],"partially":[87],"collaborative":[88],"(PCC)":[90],"algorithm":[91,151],"proposed":[93,136,149],"by":[94,134],"switching":[95,146],"training":[98],"between":[99,112],"two":[100],"models":[101],"that":[102,128],"are":[103,132],"maintained":[104],"each":[106],"FAP,":[107],"achieve":[109],"balance":[111],"users'":[114],"specific":[115],"local":[116],"characteristics":[117],"and":[118],"holistic":[119],"characteristics.":[121],"Experiments":[122],"on":[123],"real-world":[125],"dataset":[126],"demonstrate":[127],"significant":[129],"gains":[131],"achieved":[133],"FDRL":[137],"terms":[139],"CHR.":[141],"Furthermore,":[142],"with":[143,154],"an":[144],"appropriate":[145],"factor,":[147],"PCC":[150],"outperforms":[152],"FL":[153],"full":[155],"collaboration":[156],"among":[157],"FAPs.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}