{"id":"https://openalex.org/W4406612851","doi":"https://doi.org/10.1109/smc54092.2024.10831995","title":"FedAMKD: Adaptive Mutual Knowledge Distillation Federated Learning Approach for Data Quantity-Skewed Heterogeneity","display_name":"FedAMKD: Adaptive Mutual Knowledge Distillation Federated Learning Approach for Data Quantity-Skewed Heterogeneity","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4406612851","doi":"https://doi.org/10.1109/smc54092.2024.10831995"},"language":"en","primary_location":{"id":"doi:10.1109/smc54092.2024.10831995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5107955919","display_name":"Shujie Ge","orcid":null},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shujie Ge","raw_affiliation_strings":["School of Information, Beijing Wuzi University,Beijing,China,101100"],"affiliations":[{"raw_affiliation_string":"School of Information, Beijing Wuzi University,Beijing,China,101100","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056342527","display_name":"Detian Liu","orcid":"https://orcid.org/0000-0001-9949-2452"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Detian Liu","raw_affiliation_strings":["Faculty of Information Technology,Beijing,China,100124"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology,Beijing,China,100124","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078313753","display_name":"Yongli Yang","orcid":"https://orcid.org/0000-0001-8260-3611"},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongli Yang","raw_affiliation_strings":["School of Information, Beijing Wuzi University,Beijing,China,101100"],"affiliations":[{"raw_affiliation_string":"School of Information, Beijing Wuzi University,Beijing,China,101100","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100975758","display_name":"Jianyu He","orcid":null},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianyu He","raw_affiliation_strings":["School of Information, Beijing Wuzi University,Beijing,China,101100"],"affiliations":[{"raw_affiliation_string":"School of Information, Beijing Wuzi University,Beijing,China,101100","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100703152","display_name":"Shiqiang Zhang","orcid":"https://orcid.org/0000-0002-0065-5379"},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiqiang Zhang","raw_affiliation_strings":["School of Information, Beijing Wuzi University,Beijing,China,101100"],"affiliations":[{"raw_affiliation_string":"School of Information, Beijing Wuzi University,Beijing,China,101100","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040140600","display_name":"Yang Cao","orcid":"https://orcid.org/0000-0003-4549-5038"},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Cao","raw_affiliation_strings":["School of Information, Beijing Wuzi University,Beijing,China,101100"],"affiliations":[{"raw_affiliation_string":"School of Information, Beijing Wuzi University,Beijing,China,101100","institution_ids":["https://openalex.org/I176432857"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5107955919"],"corresponding_institution_ids":["https://openalex.org/I176432857"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24056159,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4710","last_page":"4715"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9958999752998352,"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":0.9958999752998352,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9333999752998352,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7284929752349854},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.544951319694519},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.542750358581543},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.43591374158859253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33405163884162903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3202403783798218}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7284929752349854},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.544951319694519},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.542750358581543},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.43591374158859253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33405163884162903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3202403783798218},{"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/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/smc54092.2024.10831995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2407386500","https://openalex.org/W3047363187","https://openalex.org/W3080934299","https://openalex.org/W3104631511","https://openalex.org/W4200580682","https://openalex.org/W4226101686","https://openalex.org/W4287332481","https://openalex.org/W4312676152","https://openalex.org/W4378438603","https://openalex.org/W4379113041","https://openalex.org/W4383503727","https://openalex.org/W4385609758","https://openalex.org/W4387096101","https://openalex.org/W4387105517","https://openalex.org/W6728757088","https://openalex.org/W6752029299","https://openalex.org/W6757139170","https://openalex.org/W6768632158","https://openalex.org/W6770590064","https://openalex.org/W6773552689","https://openalex.org/W6784239669"],"related_works":["https://openalex.org/W2466816617","https://openalex.org/W4298221930","https://openalex.org/W1970834875","https://openalex.org/W842936808","https://openalex.org/W3174028392","https://openalex.org/W2777914285","https://openalex.org/W4378677776","https://openalex.org/W2000517284","https://openalex.org/W2365318811","https://openalex.org/W3013363440"],"abstract_inverted_index":{"Federated":[0],"learning":[1,64,71],"enables":[2],"collaborative":[3],"training":[4,22],"across":[5,41,145],"various":[6],"clients":[7,15,27,45],"without":[8],"data":[9,12,56,83,89,117,137,156],"exposure.":[10],"However,":[11],"heterogeneity":[13,139],"among":[14],"may":[16],"degrade":[17],"system":[18],"performance.":[19],"The":[20],"divergent":[21],"goals":[23],"of":[24,97,114,124,136],"servers":[25,32],"and":[26,84,91,128],"lead":[28],"to":[29,47,52,80,155],"performance":[30],"degradation:":[31],"aim":[33],"for":[34,88,110],"a":[35,61,76,85],"global":[36,86,129],"model":[37,78,87,130],"with":[38],"improved":[39],"generalization":[40],"all":[42],"data,":[43],"whereas":[44],"seek":[46],"develop":[48],"private":[49],"models":[50],"tailored":[51,79],"their":[53],"specific":[54],"local":[55,77,107,127],"distributions.":[57],"This":[58,119],"paper":[59],"introduces":[60],"novel":[62],"federated":[63,70,141,160],"framework":[65],"named":[66],"FedAMKD.":[67],"FedAMKD":[68,98],"divides":[69],"into":[72],"two":[73],"independent":[74],"entities,":[75],"each":[81],"client's":[82,116],"aggregation":[90],"knowledge":[92,103],"sharing.":[93],"A":[94],"unique":[95],"aspect":[96],"is":[99],"its":[100],"adaptive":[101],"mutual":[102],"distillation":[104],"at":[105],"the":[106,111,115,122,133],"level,":[108],"customized":[109],"skewed":[112],"degree":[113],"quantity.":[118],"method":[120],"achieves":[121],"goal":[123],"enhancing":[125],"both":[126],"performance,":[131],"reducing":[132],"adverse":[134],"effects":[135],"quantity-skewed":[138],"in":[140,151,159],"learning.":[142],"Extensive":[143],"experiments":[144],"diverse":[146],"datasets":[147],"validate":[148],"FedAMKD's":[149],"success":[150],"addressing":[152],"challenges":[153],"related":[154],"quantity":[157],"imbalances":[158],"learning,":[161]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
