{"id":"https://openalex.org/W4414230436","doi":"https://doi.org/10.1109/tce.2025.3609547","title":"A Lightweight Split Federated Learning Approach With Dynamic Model Aggregation for Breast Cancer Prediction in Consumer-Centric Health Electronics","display_name":"A Lightweight Split Federated Learning Approach With Dynamic Model Aggregation for Breast Cancer Prediction in Consumer-Centric Health Electronics","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4414230436","doi":"https://doi.org/10.1109/tce.2025.3609547"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2025.3609547","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2025.3609547","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Transactions on Consumer Electronics","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/A5102557625","display_name":"Jiaman Li","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaman Li","raw_affiliation_strings":["Guangzhou Institute of Technology, Xidian University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0005-2339-203X","affiliations":[{"raw_affiliation_string":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025750958","display_name":"Dapeng Lan","orcid":"https://orcid.org/0000-0003-1104-5039"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dapeng Lan","raw_affiliation_strings":["Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China"],"raw_orcid":"https://orcid.org/0000-0003-1104-5039","affiliations":[{"raw_affiliation_string":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005917415","display_name":"Dawei Zhang","orcid":"https://orcid.org/0000-0002-9302-9368"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Zhang","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, China"],"raw_orcid":"https://orcid.org/0000-0002-9302-9368","affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067702855","display_name":"Jiakai Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141776","display_name":"China XD Group (China)","ror":"https://ror.org/04ceqst84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210141776"]},{"id":"https://openalex.org/I4210142339","display_name":"Xi\u2019an Children\u2019s Hospital","ror":"https://ror.org/04595zj73","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210142339"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiakai Wei","raw_affiliation_strings":["Department of Neonatology, Xi’an Children’s Hospital, Xi’an, China","Department of Neonatology, Xi’an Children’s hospital, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Neonatology, Xi’an Children’s Hospital, Xi’an, China","institution_ids":["https://openalex.org/I4210142339"]},{"raw_affiliation_string":"Department of Neonatology, Xi’an Children’s hospital, Xi’an, China","institution_ids":["https://openalex.org/I4210142339","https://openalex.org/I4210141776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089555942","display_name":"Hui Han","orcid":"https://orcid.org/0000-0003-3811-5682"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Han","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi’an, China","School of Communication Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0003-3811-5682","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Communication Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100349496","display_name":"Lei Liu","orcid":"https://orcid.org/0000-0001-8173-0408"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Liu","raw_affiliation_strings":["Guangzhou Institute of Technology, Xidian University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8173-0408","affiliations":[{"raw_affiliation_string":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111598130","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0002-4971-5029"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Guangzhou Institute of Technology, Xidian University, Guangzhou, China","School of Communication Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-4971-5029","affiliations":[{"raw_affiliation_string":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Communication Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11517976,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"71","issue":"4","first_page":"11371","last_page":"11382"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8151000142097473,"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/T10862","display_name":"AI in cancer detection","score":0.8151000142097473,"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/T13748","display_name":"Advanced Statistical Modeling Techniques","score":0.7534000277519226,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7019000053405762},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6621000170707703},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.414000004529953},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.3898000121116638},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.3409999907016754},{"id":"https://openalex.org/keywords/data-aggregator","display_name":"Data aggregator","score":0.3246999979019165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8141000270843506},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7019000053405762},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6621000170707703},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.35740000009536743},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3564000129699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.3246999979019165},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.27230000495910645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2025.3609547","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2025.3609547","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1769398006","display_name":"\u8d85\u89c6\u8ddd\u611f\u77e5\u6761\u4ef6\u4e0b\u7f51\u8054\u81ea\u52a8\u9a7e\u9a76\u8f66\u8def\u534f\u540c\u4fe1\u606f\u878d\u5408\u673a\u5236\u4e0e\u7b97\u6cd5\u7814\u7a76","funder_award_id":"62072360","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3494558276","display_name":null,"funder_award_id":"62172438","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6810377529","display_name":null,"funder_award_id":"2023-JCJQ-LB-007","funder_id":"https://openalex.org/F4320328373","funder_display_name":"National Key Laboratory 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/F4320328373","display_name":"National Key Laboratory Foundation of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2006961769","https://openalex.org/W2404901863","https://openalex.org/W2963209930","https://openalex.org/W3018102029","https://openalex.org/W3027472889","https://openalex.org/W3097205619","https://openalex.org/W3128646645","https://openalex.org/W4282924988","https://openalex.org/W4317498947","https://openalex.org/W4327522282","https://openalex.org/W4386243227","https://openalex.org/W4392821171","https://openalex.org/W4392883648","https://openalex.org/W4392907478","https://openalex.org/W4392910504","https://openalex.org/W4393156131","https://openalex.org/W4395097503","https://openalex.org/W4396667208","https://openalex.org/W4399665919","https://openalex.org/W4399767265","https://openalex.org/W4401541943","https://openalex.org/W4402753613","https://openalex.org/W4404788609","https://openalex.org/W4406322187","https://openalex.org/W4406891751","https://openalex.org/W4406973645","https://openalex.org/W4407690623","https://openalex.org/W4407937987","https://openalex.org/W4409882752","https://openalex.org/W4410341453"],"related_works":[],"abstract_inverted_index":{"With":[0],"disease":[1],"screening":[2],"services":[3],"increasingly":[4],"becoming":[5],"accessible":[6],"to":[7,15,68,121,177],"individual":[8],"users,":[9],"it":[10,73],"is":[11],"of":[12,170],"utmost":[13],"importance":[14],"ensure":[16],"accurate":[17],"and":[18,52,82,113,131,162,187],"efficient":[19],"risk":[20],"prediction":[21],"while":[22],"protecting":[23],"sensitive":[24],"medical":[25,38],"information":[26],"in":[27,37,172],"consumer-oriented":[28],"health":[29],"electronics.":[30],"Although":[31],"deep":[32],"learning":[33,56,180],"shows":[34],"great":[35],"potential":[36],"image":[39],"analysis,":[40],"centralized":[41],"methodologies":[42],"encounter":[43],"hurdles,":[44],"including":[45],"high":[46],"privacy":[47,133],"risks,":[48],"substantial":[49],"computational":[50,124],"demands,":[51],"limited":[53],"scalability.":[54],"Federated":[55,94],"addresses":[57],"these":[58,87],"limitations":[59],"by":[60,99,149],"enabling":[61],"collaborative":[62],"model":[63],"training":[64,160,190],"without":[65],"the":[66,146,168],"need":[67],"exchange":[69],"raw":[70],"data.":[71],"However,":[72],"still":[74],"suffers":[75],"from":[76],"client":[77,140],"heterogeneity,":[78],"inadequate":[79],"communication":[80,129],"efficiency,":[81,130],"sluggish":[83],"convergence.":[84],"To":[85],"address":[86],"challenges,":[88],"we":[89,137],"propose":[90],"a":[91,100,139],"novel":[92],"Split":[93],"Learning":[95],"(SFL)":[96],"approach":[97],"enhanced":[98],"Lightweight":[101],"Dynamic":[102],"Model":[103],"Aggregation":[104],"strategy":[105,148],"(named":[106],"as":[107],"LDA-SFL),":[108],"tailored":[109],"explicitly":[110],"for":[111],"resource-constrained":[112],"privacy-sensitive":[114],"healthcare":[115],"scenarios.":[116],"Our":[117],"method":[118],"leverages":[119],"SFL":[120],"effectively":[122],"balance":[123],"loads":[125],"across":[126],"devices,":[127],"improve":[128],"strengthen":[132],"protection.":[134],"In":[135],"addition,":[136],"design":[138],"selection":[141],"mechanism":[142],"that":[143,153],"dynamically":[144],"adjusts":[145],"aggregation":[147,185],"filtering":[150],"out":[151],"models":[152],"may":[154],"degrade":[155],"global":[156],"performance,":[157],"thereby":[158],"improving":[159],"efficiency":[161],"overall":[163,189],"performance.":[164],"Extensive":[165],"experiments":[166],"demonstrate":[167],"effectiveness":[169],"LDA-SFL":[171,182],"breast":[173],"cancer":[174],"prediction.":[175],"Compared":[176],"conventional":[178],"distributed":[179],"methods,":[181],"significantly":[183],"reduces":[184],"overhead":[186],"improves":[188],"efficiency.":[191]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}