{"id":"https://openalex.org/W4403863667","doi":"https://doi.org/10.1109/tr.2024.3480117","title":"UAV-Enabled Energy-Efficient Aerial Computing: A Federated Deep Reinforcement Learning Approach","display_name":"UAV-Enabled Energy-Efficient Aerial Computing: A Federated Deep Reinforcement Learning Approach","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4403863667","doi":"https://doi.org/10.1109/tr.2024.3480117"},"language":"en","primary_location":{"id":"doi:10.1109/tr.2024.3480117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tr.2024.3480117","pdf_url":null,"source":{"id":"https://openalex.org/S87725633","display_name":"IEEE Transactions on Reliability","issn_l":"0018-9529","issn":["0018-9529","1558-1721"],"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 Reliability","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/A5101932319","display_name":"Qianqian Wu","orcid":"https://orcid.org/0000-0001-8467-4034"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianqian Wu","raw_affiliation_strings":["Beijing Key Lab of Transportation Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8467-4034","affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Transportation Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409425","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0001-5735-8642"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["Beijing Key Lab of Transportation Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5735-8642","affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Transportation Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028388820","display_name":"Ying He","orcid":"https://orcid.org/0000-0003-1603-9375"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ying He","raw_affiliation_strings":["School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-1603-9375","affiliations":[{"raw_affiliation_string":"School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100594935","display_name":"Zefan Wu","orcid":"https://orcid.org/0009-0002-7381-2491"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zefan Wu","raw_affiliation_strings":["Beijing Key Lab of Transportation Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Transportation Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.6584,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.94722715,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"74","issue":"3","first_page":"3559","last_page":"3572"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11489","display_name":"Air Traffic Management and Optimization","score":0.9681000113487244,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9617999792098999,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7939040064811707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7291338443756104},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.448900431394577},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.41327965259552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4119647145271301}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7939040064811707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7291338443756104},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.448900431394577},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.41327965259552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4119647145271301},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tr.2024.3480117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tr.2024.3480117","pdf_url":null,"source":{"id":"https://openalex.org/S87725633","display_name":"IEEE Transactions on Reliability","issn_l":"0018-9529","issn":["0018-9529","1558-1721"],"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 Reliability","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8399999737739563,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G5649489718","display_name":null,"funder_award_id":"2023YJS104","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2963555336","https://openalex.org/W2982461188","https://openalex.org/W2991268436","https://openalex.org/W3029840414","https://openalex.org/W3043715968","https://openalex.org/W3081323070","https://openalex.org/W3089190064","https://openalex.org/W3090837869","https://openalex.org/W3127743396","https://openalex.org/W3130992186","https://openalex.org/W3135497297","https://openalex.org/W3191425715","https://openalex.org/W3211531747","https://openalex.org/W4206631234","https://openalex.org/W4211236403","https://openalex.org/W4225923531","https://openalex.org/W4226189128","https://openalex.org/W4226215778","https://openalex.org/W4226478636","https://openalex.org/W4285245017","https://openalex.org/W4286579117","https://openalex.org/W4288768831","https://openalex.org/W4312315462","https://openalex.org/W4376878836","https://openalex.org/W4389104924"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4306904969","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2138720691","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Aerial":[0],"computing":[1],"paradigms,":[2],"particularly":[3],"those":[4],"involving":[5],"UAVs":[6,56,88,109,145],"as":[7],"access":[8,26],"points":[9],"and":[10,20,43,66,141,169,177],"radio":[11],"towers,":[12],"show":[13],"significant":[14,49],"promise":[15],"for":[16,110],"local":[17],"data":[18],"analysis":[19],"real-time":[21],"service":[22],"provision":[23],"in":[24,80,157],"aerial":[25],"networks.":[27,159],"However,":[28],"the":[29,37,69,84,127,138,153],"limited":[30],"battery":[31],"life":[32],"of":[33,40,73,87,115,129,144,155],"UAVs,":[34],"compounded":[35],"by":[36,175],"high-energy":[38],"demands":[39],"communication":[41],"tasks":[42],"prolonged":[44,70],"computation":[45,139],"delays,":[46],"presents":[47],"a":[48,97],"challenge.":[50],"Deep":[51],"reinforcement":[52],"learning":[53,71,100],"(DRL)":[54],"enables":[55],"to":[57,77,107,146,162],"autonomously":[58],"optimize":[59],"their":[60],"operations,":[61],"reducing":[62],"both":[63],"energy":[64],"consumption":[65],"latency.":[67],"Nevertheless,":[68],"process":[72],"DRL":[74,130,165],"can":[75],"lead":[76],"inefficiencies,":[78],"especially":[79],"dynamic":[81],"environments":[82],"where":[83],"equitable":[85,113],"participation":[86],"is":[89],"crucial.":[90],"To":[91],"address":[92],"these":[93],"issues,":[94],"we":[95,120],"introduce":[96],"fairness-oriented":[98],"federated":[99],"(FL)":[101],"scheme":[102,125],"that":[103],"employs":[104],"importance":[105],"sampling":[106],"select":[108],"training,":[111],"ensuring":[112],"utilization":[114],"each":[116],"UAV's":[117],"data.":[118],"Furthermore,":[119],"integrate":[121],"this":[122],"FL":[123],"fairness":[124,154],"into":[126],"design":[128],"algorithms,":[131],"termed":[132],"FedDRL.":[133],"This":[134],"algorithm":[135],"jointly":[136],"optimizes":[137],"capabilities":[140],"bandwidth":[142],"allocation":[143],"minimize":[147],"system":[148,173],"costs.":[149],"Numerical":[150],"results":[151],"demonstrate":[152],"FedDRL":[156,171],"fFL":[158],"Specifically,":[160],"compared":[161],"other":[163],"state-of-the-art":[164],"algorithms":[166],"(e.g.,":[167],"TD3":[168],"DDPG),":[170],"reduces":[172],"costs":[174],"43.82%":[176],"49.45%,":[178],"respectively.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}