{"id":"https://openalex.org/W4416965719","doi":"https://doi.org/10.1109/tmc.2025.3639671","title":"Seizing Critical Learning Period in UAV-Assisted Hierarchical Personalized Federated Learning","display_name":"Seizing Critical Learning Period in UAV-Assisted Hierarchical Personalized Federated Learning","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W4416965719","doi":"https://doi.org/10.1109/tmc.2025.3639671"},"language":null,"primary_location":{"id":"doi:10.1109/tmc.2025.3639671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3639671","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Mobile Computing","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/A5061337923","display_name":"Yanlu Li","orcid":"https://orcid.org/0009-0000-8976-301X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanlu Li","raw_affiliation_strings":["State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-8976-301X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100449904","display_name":"Yiming Liu","orcid":"https://orcid.org/0000-0001-8824-4007"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Liu","raw_affiliation_strings":["State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8824-4007","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034586871","display_name":"Yuzhen Huang","orcid":"https://orcid.org/0000-0002-9536-7918"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuzhen Huang","raw_affiliation_strings":["Academy of Military Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9536-7918","affiliations":[{"raw_affiliation_string":"Academy of Military Sciences, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100410680","display_name":"Zhi Zhang","orcid":"https://orcid.org/0000-0001-8672-6766"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Zhang","raw_affiliation_strings":["State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8672-6766","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.8188,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.9605812,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"25","issue":"5","first_page":"6740","last_page":"6754"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.5299000144004822,"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":0.5299000144004822,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.08259999752044678,"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":0.03819999843835831,"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/metric","display_name":"Metric (unit)","score":0.6543999910354614},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.641700029373169},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.588100016117096},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.5236999988555908},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5163000226020813},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.49300000071525574},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4544999897480011},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.4496999979019165},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.37059998512268066}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8393999934196472},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6543999910354614},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.641700029373169},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.588100016117096},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49410000443458557},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.49300000071525574},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4408000111579895},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.35920000076293945},{"id":"https://openalex.org/C2777480716","wikidata":"https://www.wikidata.org/wiki/Q23582796","display_name":"Resource consumption","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.262800008058548},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2025.3639671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3639671","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5029153023","display_name":null,"funder_award_id":"L251036","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G6785865023","display_name":null,"funder_award_id":"62471065","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"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1988456768","https://openalex.org/W2112796928","https://openalex.org/W2225156818","https://openalex.org/W2604830243","https://openalex.org/W2962691117","https://openalex.org/W2962804513","https://openalex.org/W2963318081","https://openalex.org/W2964023906","https://openalex.org/W3023338037","https://openalex.org/W3029840414","https://openalex.org/W3045638580","https://openalex.org/W3090615085","https://openalex.org/W3093755649","https://openalex.org/W3105649577","https://openalex.org/W3118154863","https://openalex.org/W4206770380","https://openalex.org/W4225553110","https://openalex.org/W4283790910","https://openalex.org/W4286375442","https://openalex.org/W4313043980","https://openalex.org/W4315629847","https://openalex.org/W4321608017","https://openalex.org/W4376878836","https://openalex.org/W4382203046","https://openalex.org/W4384158407","https://openalex.org/W4385568263","https://openalex.org/W4386362921","https://openalex.org/W4389230743","https://openalex.org/W4390872667","https://openalex.org/W4391547738","https://openalex.org/W4402661241","https://openalex.org/W4406417530","https://openalex.org/W4409917263","https://openalex.org/W7133216270"],"related_works":[],"abstract_inverted_index":{"Federated":[0,125,161],"learning":[1,35,47,98,197],"(FL)":[2],"is":[3,42],"suitable":[4],"for":[5,139,177],"unmanned":[6],"aerial":[7],"vehicles":[8],"(UAVs)":[9],"and":[10,18,61,68,100,112,188,222],"ground":[11],"devices":[12,71],"to":[13,37,58,166,185,200,217],"exchange":[14],"model":[15,22,114,189,214],"parameters":[16],"periodically":[17],"learn":[19],"a":[20,90,124,158,194],"shared":[21],"without":[23],"transmitting":[24],"raw":[25],"data.":[26],"However,":[27],"the":[28,45,53,74,77,82,107,118,134,145],"existing":[29],"UAV-assisted":[30],"FL":[31,54,92,103],"systems":[32],"consider":[33],"all":[34],"phases":[36],"be":[38],"equally":[39],"important,":[40],"which":[41],"inconsistent":[43],"with":[44],"critical":[46],"period":[48],"(CLP)":[49],"that":[50,94,132,206],"exists":[51],"in":[52,85,117,152],"training":[55,104,120],"process,":[56],"leading":[57],"significant":[59,108],"overheads":[60],"inefficient":[62],"resource":[63],"utilization.":[64],"Besides,":[65],"data":[66,146],"heterogeneity":[67],"insufficiency":[69],"among":[70],"also":[72,156],"affect":[73],"performance":[75],"of":[76,170],"trained":[78],"models.":[79],"To":[80,143],"address":[81],"above":[83],"issues,":[84],"this":[86],"paper,":[87],"we":[88,122,155],"propose":[89],"CLP-aware":[91],"framework":[93],"identifies":[95],"unequally":[96],"important":[97],"stages":[99],"implements":[101],"corresponding":[102],"strategies.":[105],"Given":[106],"differences":[109],"between":[110,136],"global":[111],"local":[113],"parameter":[115],"distributions":[116,138],"early":[119],"epochs,":[121],"introduce":[123],"Kullback-Leibler":[126],"divergence":[127],"(KLD)":[128],"Norm":[129,163],"(FKN)":[130],"metric":[131,165],"measures":[133],"KLD":[135],"these":[137],"efficient":[140,160],"CLP":[141],"detection.":[142],"capture":[144],"drift":[147],"caused":[148],"by":[149],"environmental":[150],"shift":[151],"UAV":[153,182],"swarms,":[154],"develop":[157],"computationally":[159],"Drift":[162],"(FDN)":[164],"enable":[167],"online":[168],"detection":[169],"CLP.":[171],"We":[172],"formulate":[173],"an":[174],"optimization":[175],"problem":[176],"CLP-based":[178],"participating":[179],"device":[180],"selection,":[181],"visit":[183],"frequencies":[184],"different":[186],"devices,":[187],"aggregation":[190],"period,":[191],"then":[192],"adopt":[193],"deep":[195],"reinforcement":[196],"(DRL)-based":[198],"algorithm":[199],"solve":[201],"it.":[202],"Simulation":[203],"results":[204],"show":[205],"our":[207],"strategy":[208],"reduces":[209],"energy":[210],"consumption":[211],"while":[212],"maintaining":[213],"accuracy":[215],"compared":[216],"baselines,":[218],"i.e.,":[219],"CriticalFL,":[220],"pFedBayes,":[221],"FedExp.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-12-03T00:00:00"}