{"id":"https://openalex.org/W4402351105","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650954","title":"pFedBEA: Combatting Data Heterogeneity for Personalized Federated Learning by Body Exchange and Aggregation Abandon","display_name":"pFedBEA: Combatting Data Heterogeneity for Personalized Federated Learning by Body Exchange and Aggregation Abandon","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351105","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650954"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650954","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/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":true,"raw_author_name":"Jianyu He","raw_affiliation_strings":["Beijing Wuzi University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Wuzi University,Beijing,China","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/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Detian Liu","raw_affiliation_strings":["Beijing University of Technology,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Beijing,China","institution_ids":["https://openalex.org/I37796252"]}]},{"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":["Beijing Wuzi University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Wuzi University,Beijing,China","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","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":false,"raw_author_name":"Shujie Ge","raw_affiliation_strings":["Beijing Wuzi University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Wuzi University,Beijing,China","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","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":["Beijing Wuzi University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Wuzi University,Beijing,China","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055567445","display_name":"Hengliang Tang","orcid":"https://orcid.org/0000-0001-6747-2369"},"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":"Hengliang Tang","raw_affiliation_strings":["Beijing Wuzi University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Wuzi University,Beijing,China","institution_ids":["https://openalex.org/I176432857"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100975758"],"corresponding_institution_ids":["https://openalex.org/I176432857"],"apc_list":null,"apc_paid":null,"fwci":0.7274,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75968369,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T10237","display_name":"Cryptography and Data Security","score":0.9703999757766724,"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":0.965499997138977,"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":0.6767536401748657},{"id":"https://openalex.org/keywords/data-aggregator","display_name":"Data aggregator","score":0.47295233607292175},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.33308660984039307},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11626449227333069}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6767536401748657},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.47295233607292175},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.33308660984039307},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11626449227333069},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650954","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.4000000059604645,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2807006176","https://openalex.org/W2903471046","https://openalex.org/W2954124071","https://openalex.org/W2963819344","https://openalex.org/W2976335444","https://openalex.org/W2990789643","https://openalex.org/W3006360344","https://openalex.org/W3015636663","https://openalex.org/W3035453001","https://openalex.org/W3037871107","https://openalex.org/W3080934299","https://openalex.org/W3099314130","https://openalex.org/W3112044954","https://openalex.org/W3124515033","https://openalex.org/W3129603732","https://openalex.org/W3133814152","https://openalex.org/W3136022984","https://openalex.org/W3168269241","https://openalex.org/W3182158470","https://openalex.org/W3200318570","https://openalex.org/W3213815372","https://openalex.org/W3214056483","https://openalex.org/W4281753687","https://openalex.org/W4283796083","https://openalex.org/W4385338552","https://openalex.org/W6728757088","https://openalex.org/W6752029299","https://openalex.org/W6757139170","https://openalex.org/W6759238902","https://openalex.org/W6768570320","https://openalex.org/W6770590064","https://openalex.org/W6773817997","https://openalex.org/W6780224944","https://openalex.org/W6784336702","https://openalex.org/W6789100154","https://openalex.org/W6789305514","https://openalex.org/W6790034021","https://openalex.org/W6791102956","https://openalex.org/W6796096428","https://openalex.org/W6796504275","https://openalex.org/W6803116340"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2793666424","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Data":[0],"heterogeneity":[1,53],"caused":[2],"by":[3],"Non-Independent":[4],"and":[5,15,20,87,98,108,129,152,177],"Identically":[6],"Distributed":[7],"(non-IID)":[8],"data":[9,124],"in":[10],"local":[11,118],"clients":[12],"imposes":[13],"limitations":[14],"challenges":[16],"on":[17,27,32,163],"the":[18,46,52,79,104,110,131,135,147],"training":[19],"performance":[21,143],"of":[22,54,134,149,166],"Federated":[23],"Learning.":[24],"Current":[25],"researches":[26],"this":[28],"issue":[29],"mainly":[30],"focus":[31],"optimizing":[33],"a":[34,68,83,88,164],"single":[35],"global":[36,105,136],"model":[37,41,81,85,90,142],"or":[38],"developing":[39],"personalized":[40,70,99],"for":[42],"each":[43],"client,":[44],"neglecting":[45],"essential":[47],"client":[48,55,80],"relationships.":[49],"However,":[50],"despite":[51],"data,":[56],"common":[57,96],"characteristics":[58],"that":[59,169],"can":[60],"be":[61],"leveraged":[62],"still":[63],"exist.":[64],"Therefore,":[65],"We":[66,159],"proposed":[67],"novel":[69],"federated":[71],"learning":[72,121],"approach,":[73],"called":[74],"pFedBEA.":[75],"The":[76],"method":[77],"decomposes":[78],"into":[82],"body":[84,111],"(extractor)":[86],"head":[89],"(classifier)":[91],"to":[92,95],"respectively":[93],"adapt":[94],"features":[97],"attributes.":[100],"Subsequently,":[101],"periodically":[102],"abandoning":[103],"server":[106],"aggregation":[107,150,175],"exchanging":[109],"models":[112],"among":[113],"different":[114],"clients,":[115],"which":[116],"enables":[117],"personalization":[119],"while":[120],"from":[122],"multiple":[123],"sources,":[125],"promoting":[126],"knowledge":[127],"sharing":[128],"enhancing":[130],"generalization":[132],"ability":[133],"model.":[137],"pFedBEA":[138,170],"not":[139],"only":[140],"improves":[141],"but":[144],"also":[145],"reduces":[146],"number":[148],"rounds":[151],"communication":[153,178],"time,":[154],"achieving":[155],"overall":[156],"efficiency":[157,176],"optimization.":[158],"conducted":[160],"extensive":[161],"experiments":[162],"range":[165],"datasets,":[167],"demonstrating":[168],"achieves":[171],"higher":[172],"accuracy,":[173],"superior":[174],"efficiency.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}