{"id":"https://openalex.org/W4312379976","doi":"https://doi.org/10.1109/jstsp.2022.3223526","title":"Knowledge Selection and Local Updating Optimization for Federated Knowledge Distillation With Heterogeneous Models","display_name":"Knowledge Selection and Local Updating Optimization for Federated Knowledge Distillation With Heterogeneous Models","publication_year":2022,"publication_date":"2022-11-21","ids":{"openalex":"https://openalex.org/W4312379976","doi":"https://doi.org/10.1109/jstsp.2022.3223526"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2022.3223526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2022.3223526","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","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/A5100430756","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-2136-5186"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I4210099312","display_name":"Shandong Jiaotong University","ror":"https://ror.org/01848hk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["Cooperative Medianet Innovation Center (CMIC) and the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2136-5186","affiliations":[{"raw_affiliation_string":"Cooperative Medianet Innovation Center (CMIC) and the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I4210099312","https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091097798","display_name":"Naifu Zhang","orcid":"https://orcid.org/0000-0003-2586-3841"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I4210099312","display_name":"Shandong Jiaotong University","ror":"https://ror.org/01848hk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naifu Zhang","raw_affiliation_strings":["Cooperative Medianet Innovation Center (CMIC) and the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2586-3841","affiliations":[{"raw_affiliation_string":"Cooperative Medianet Innovation Center (CMIC) and the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I4210099312","https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016127527","display_name":"Meixia Tao","orcid":"https://orcid.org/0000-0002-0799-0954"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I4210099312","display_name":"Shandong Jiaotong University","ror":"https://ror.org/01848hk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meixia Tao","raw_affiliation_strings":["Cooperative Medianet Innovation Center (CMIC) and the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0799-0954","affiliations":[{"raw_affiliation_string":"Cooperative Medianet Innovation Center (CMIC) and the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I4210099312","https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100385692","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0001-9943-6020"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Sun Yat-sen University, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-9943-6020","affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2197,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.89713571,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"17","issue":"1","first_page":"82","last_page":"97"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9944000244140625,"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":0.9857000112533569,"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":0.8339544534683228},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.705054521560669},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.564784049987793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5173132419586182},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5110117197036743},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49157223105430603},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.4664663076400757},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.45191556215286255},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43615198135375977},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4246067702770233}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8339544534683228},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.705054521560669},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.564784049987793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5173132419586182},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5110117197036743},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49157223105430603},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.4664663076400757},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45191556215286255},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43615198135375977},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4246067702770233},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstsp.2022.3223526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2022.3223526","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G3886632535","display_name":null,"funder_award_id":"62125108","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1751687266","https://openalex.org/W1821462560","https://openalex.org/W2134797427","https://openalex.org/W2294370754","https://openalex.org/W2807731816","https://openalex.org/W2809090039","https://openalex.org/W2903471046","https://openalex.org/W2921434559","https://openalex.org/W2955213239","https://openalex.org/W2955338161","https://openalex.org/W2962883549","https://openalex.org/W2980216952","https://openalex.org/W2981096252","https://openalex.org/W2995022099","https://openalex.org/W3035453001","https://openalex.org/W3038022836","https://openalex.org/W3059999910","https://openalex.org/W3090615085","https://openalex.org/W3096572172","https://openalex.org/W3103657382","https://openalex.org/W3122967774","https://openalex.org/W3130025260","https://openalex.org/W3178336997","https://openalex.org/W3186051974","https://openalex.org/W3193066552","https://openalex.org/W3211894487","https://openalex.org/W4289436733","https://openalex.org/W4317794926","https://openalex.org/W4389496940","https://openalex.org/W6638523607","https://openalex.org/W6679909955","https://openalex.org/W6728757088","https://openalex.org/W6752495264","https://openalex.org/W6755150206","https://openalex.org/W6757139170","https://openalex.org/W6759238902","https://openalex.org/W6760214840","https://openalex.org/W6762811270","https://openalex.org/W6763048141","https://openalex.org/W6765541894","https://openalex.org/W6768632158","https://openalex.org/W6773817997","https://openalex.org/W6780224944","https://openalex.org/W6782317661","https://openalex.org/W6790595243","https://openalex.org/W6796484261","https://openalex.org/W6798417033","https://openalex.org/W6980041674"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"Federated":[0],"learning":[1,7],"(FL)":[2],"is":[3,56,71,112,131],"a":[4,18,39,141],"promising":[5],"distributed":[6],"paradigm":[8],"in":[9,33,61,68,82,156],"which":[10],"multiple":[11],"edge":[12],"devices":[13],"(EDs)":[14],"collaborate":[15],"to":[16,42,65,73,114,133,144,158],"train":[17],"shared":[19],"model":[20,45,52,60,93,148],"without":[21],"exchanging":[22],"privacy-sensitive":[23],"raw":[24],"data.":[25],"With":[26],"the":[27,62,88,97,116,146,160,174,178,183,199,204],"heterogeneous":[28],"architectures":[29],"of":[30,51,163,203],"local":[31,92,147,164],"models":[32,165,194],"FL,":[34,69],"knowledge":[35,49,76,98,121],"distillation":[36,180],"(KD)":[37],"offers":[38],"new":[40],"way":[41],"deal":[43],"with":[44],"heterogeneity":[46],"by":[47,100,167],"aggregating":[48],"instead":[50],"parameters.":[53],"Usually,":[54],"there":[55],"no":[57],"pre-trained":[58],"teacher":[59],"FL":[63,184],"setup,":[64],"apply":[66],"KD":[67,152],"it":[70],"critical":[72],"design":[74],"efficient":[75,120],"aggregation":[77],"mechanism.":[78],"To":[79],"this":[80,83,110,135],"end,":[81],"paper,":[84],"we":[85,139,171],"first":[86],"analyze":[87],"relationship":[89,111],"between":[90],"each":[91,154],"convergence":[94],"rate":[95],"and":[96,177,195],"generated":[99],"selecting":[101],"predicted":[102,117,127],"logits.":[103],"Then,":[104],"an":[105],"optimization":[106],"problem":[107],"based":[108,188],"on":[109,189],"formulated":[113],"schedule":[115],"logits":[118,128],"for":[119,153],"aggregation.":[122],"An":[123],"iterative":[124],"algorithm":[125],"called":[126],"selection":[129,176],"(PLS)":[130],"designed":[132],"solve":[134],"problem.":[136],"After":[137],"that,":[138],"propose":[140],"threshold-based":[142],"technique":[143],"optimize":[145,172],"updating":[149],"options":[150],"with/without":[151],"ED":[155],"order":[157],"limit":[159],"performance":[161,201],"degradation":[162],"caused":[166],"misleading":[168],"knowledge.":[169],"Meanwhile,":[170],"both":[173],"threshold":[175],"reasonable":[179],"intensity":[181],"during":[182],"process.":[185],"Extensive":[186],"experiments":[187],"convolutional":[190],"neural":[191],"network":[192],"(CNN)":[193],"real-world":[196],"datasets":[197],"demonstrate":[198],"superior":[200],"gain":[202],"proposed":[205],"approach":[206],"over":[207],"existing":[208],"benchmark":[209],"methods.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}