{"id":"https://openalex.org/W7127587925","doi":"https://doi.org/10.1109/ccnc65079.2026.11366464","title":"PP-CLS-FL: Privacy-Preserving CLS-Transfer Federated Learning on Heterogeneous Clients","display_name":"PP-CLS-FL: Privacy-Preserving CLS-Transfer Federated Learning on Heterogeneous Clients","publication_year":2026,"publication_date":"2026-01-09","ids":{"openalex":"https://openalex.org/W7127587925","doi":"https://doi.org/10.1109/ccnc65079.2026.11366464"},"language":null,"primary_location":{"id":"doi:10.1109/ccnc65079.2026.11366464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications &amp;amp; Networking Conference (CCNC)","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/A5125044388","display_name":"Feras Shoukeir","orcid":null},"institutions":[{"id":"https://openalex.org/I75256744","display_name":"Tennessee State University","ror":"https://ror.org/01fpczx89","country_code":"US","type":"education","lineage":["https://openalex.org/I75256744"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Feras Shoukeir","raw_affiliation_strings":["Tennessee State University,Nashville,TN,USA"],"affiliations":[{"raw_affiliation_string":"Tennessee State University,Nashville,TN,USA","institution_ids":["https://openalex.org/I75256744"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125091591","display_name":"Liang Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I75256744","display_name":"Tennessee State University","ror":"https://ror.org/01fpczx89","country_code":"US","type":"education","lineage":["https://openalex.org/I75256744"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Hong","raw_affiliation_strings":["Tennessee State University,Nashville,TN,USA"],"affiliations":[{"raw_affiliation_string":"Tennessee State University,Nashville,TN,USA","institution_ids":["https://openalex.org/I75256744"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121732603","display_name":"Kamrul Hasan","orcid":null},"institutions":[{"id":"https://openalex.org/I75256744","display_name":"Tennessee State University","ror":"https://ror.org/01fpczx89","country_code":"US","type":"education","lineage":["https://openalex.org/I75256744"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kamrul Hasan","raw_affiliation_strings":["Tennessee State University,Nashville,TN,USA"],"affiliations":[{"raw_affiliation_string":"Tennessee State University,Nashville,TN,USA","institution_ids":["https://openalex.org/I75256744"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5125044388"],"corresponding_institution_ids":["https://openalex.org/I75256744"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27661177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7473000288009644,"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":0.7473000288009644,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.04989999905228615,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.01489999983459711,"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/federated-learning","display_name":"Federated learning","score":0.8632000088691711},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6240000128746033},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5831999778747559},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.47920000553131104},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.45660001039505005},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.451200008392334},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.4438999891281128},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3790000081062317},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.34929999709129333},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3447999954223633}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.8632000088691711},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8206999897956848},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6240000128746033},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5831999778747559},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.45660001039505005},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.451200008392334},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.42669999599456787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41609999537467957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39719998836517334},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3790000081062317},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3336000144481659},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.28200000524520874},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25099998712539673},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccnc65079.2026.11366464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications &amp;amp; Networking Conference (CCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2473418344","https://openalex.org/W2767079719","https://openalex.org/W2798720628","https://openalex.org/W3012501605","https://openalex.org/W3045674654","https://openalex.org/W3138516171","https://openalex.org/W3159481202","https://openalex.org/W3182158470","https://openalex.org/W4206320562","https://openalex.org/W4312443924","https://openalex.org/W4313156423"],"related_works":[],"abstract_inverted_index":{"Federated":[0,30,85],"learning":[1],"(FL)":[2],"is":[3,247],"a":[4,40,88,102,147,156,166,180],"decentralized":[5],"paradigm":[6],"that":[7,90,140,150,193],"enables":[8],"collaborative":[9],"model":[10,36],"training":[11,132,153],"across":[12],"multiple":[13],"institutions\u2014such":[14],"as":[15],"hospitals\u2014without":[16],"sharing":[17],"sensitive":[18],"medical":[19,97,259],"data":[20,53,176],"(e.g.,":[21],"MRI":[22,182],"or":[23,226],"CT":[24],"scans).":[25],"The":[26,245],"most":[27],"common":[28],"algorithm,":[29],"Averaging":[31],"(FedAvg),":[32],"aggregates":[33],"locally":[34],"trained":[35],"weights":[37],"to":[38,73,164,211,221],"form":[39],"global":[41,142],"model.":[42],"However,":[43],"FedAvg":[44,194],"struggles":[45],"under":[46,242],"statistical":[47],"heterogeneity":[48,64],"(non-independent":[49],"and":[50,59,62,68,76,93,129,159,240,251,255],"identically":[51],"distributed":[52],"from":[54,219],"diverse":[55],"scanners,":[56],"imaging":[57],"protocols,":[58],"patient":[60],"demographics)":[61],"system":[63],"(varying":[65],"client":[66,152],"computation":[67],"memory":[69],"capacities),":[70],"often":[71],"leading":[72],"unstable":[74],"optimization":[75],"slow":[77],"convergence.To":[78],"address":[79],"these":[80],"challenges,":[81],"we":[82],"propose":[83],"CLS-Transfer":[84],"Learning":[86],"(CLS-TFL),":[87],"framework":[89,246],"improves":[91,209],"robustness":[92],"efficiency":[94],"in":[95,258],"heterogeneous":[96],"FL.":[98],"Our":[99],"approach":[100],"leverages":[101],"pretrained":[103],"Vision":[104],"Transformer":[105],"(ViT)":[106],"while":[107,169,214],"adapting":[108],"only":[109],"the":[110,116,120,137],"[CLS]":[111,138],"(classification)":[112],"token":[113],"head\u2014and":[114],"optionally":[115],"final":[117],"transformer":[118],"block\u2014keeping":[119],"main":[121],"encoder":[122],"frozen.":[123],"This":[124],"design":[125],"reduces":[126],"trainable":[127],"parameters":[128],"stabilizes":[130],"non-IID":[131,181],"by":[133],"focusing":[134],"adaptation":[135,233],"on":[136,179],"representation":[139],"summarizes":[141],"image":[143],"semantics.We":[144],"further":[145,208],"introduce":[146],"resource-aware":[148,206],"orchestrator":[149],"estimates":[151],"speeds":[154],"during":[155],"warmup":[157],"phase":[158],"dynamically":[160],"allocates":[161],"local":[162],"epochs":[163],"satisfy":[165],"time":[167,218],"budget":[168],"ensuring":[170],"adequate":[171],"sample":[172],"coverage":[173],"for":[174],"rare":[175],"types.":[177],"Experiments":[178],"classification":[183],"dataset":[184],"with":[185,234,253],"client-specific":[186],"perturbations":[187],"(blur,":[188],"noise,":[189],"brightness,":[190],"resolution)":[191],"demonstrate":[192],"achieves":[195],"73.56":[196],"%":[197,213],"test":[198],"accuracy,":[199],"whereas":[200],"CLS-TFL":[201,229],"reaches":[202],"89.49":[203],"%.":[204],"Incorporating":[205],"allocation":[207],"accuracy":[210,239],"92.29":[212],"reducing":[215],"average":[216],"round":[217],"66.10":[220],"62.40":[222],"seconds.Unlike":[223],"existing":[224],"optimizer-":[225],"client-selection-based":[227],"approaches,":[228],"unifies":[230],"ViT":[231],"[CLS]-based":[232],"time-budgeted":[235],"orchestration,":[236],"improving":[237],"both":[238],"time-to-accuracy":[241],"real-world":[243],"heterogeneity.":[244],"modular,":[248],"easily":[249],"deployable,":[250],"compatible":[252],"personalization":[254],"fairness":[256],"extensions":[257],"federated":[260],"learning.":[261]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-02-06T00:00:00"}
