{"id":"https://openalex.org/W7160625634","doi":"https://doi.org/10.1007/s10462-026-11574-x","title":"The application of federated learning in breast cancer diagnosis and treatment: a survey","display_name":"The application of federated learning in breast cancer diagnosis and treatment: a survey","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160625634","doi":"https://doi.org/10.1007/s10462-026-11574-x"},"language":"en","primary_location":{"id":"doi:10.1007/s10462-026-11574-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-026-11574-x","pdf_url":null,"source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/s10462-026-11574-x","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121560369","display_name":"Huafeng Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I182386381","display_name":"Zunyi Medical University","ror":"https://ror.org/00g5b0g93","country_code":"CN","type":"education","lineage":["https://openalex.org/I182386381"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huafeng Wu","raw_affiliation_strings":["School of Intelligence & Information Engineering, Medicine & Technology College of Zunyi Medical University, Zunyi, 563000, Guizhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligence & Information Engineering, Medicine & Technology College of Zunyi Medical University, Zunyi, 563000, Guizhou, China","institution_ids":["https://openalex.org/I182386381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083845136","display_name":"Mohsen Ghorbian","orcid":"https://orcid.org/0000-0003-2766-888X"},"institutions":[{"id":"https://openalex.org/I4210113955","display_name":"Qom Islamic Azad University","ror":"https://ror.org/0283g3v77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I4210113955"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Mohsen Ghorbian","raw_affiliation_strings":["Department of Computer Engineering, Qo.C., Islamic Azad University, Qom, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Qo.C., Islamic Azad University, Qom, Iran","institution_ids":["https://openalex.org/I4210113955"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083845136","https://openalex.org/A5121560369"],"corresponding_institution_ids":["https://openalex.org/I182386381","https://openalex.org/I4210113955"],"apc_list":{"value":3090,"currency":"USD","value_usd":3090},"apc_paid":{"value":3090,"currency":"USD","value_usd":3090},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.54519892,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"59","issue":"8","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7626000046730042,"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":0.7626000046730042,"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/T10862","display_name":"AI in cancer detection","score":0.025100000202655792,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.01810000091791153,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7558000087738037},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.7044000029563904},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.602400004863739},{"id":"https://openalex.org/keywords/confidentiality","display_name":"Confidentiality","score":0.5196999907493591},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.5149999856948853},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.41659998893737793},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.3801000118255615},{"id":"https://openalex.org/keywords/precision-medicine","display_name":"Precision medicine","score":0.37940001487731934},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3734999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775999903678894},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7558000087738037},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.7044000029563904},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.602400004863739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5706999897956848},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.5149999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.491100013256073},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C202645933","wikidata":"https://www.wikidata.org/wiki/Q4930","display_name":"eHealth","level":3,"score":0.35920000076293945},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.35040000081062317},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C32220436","wikidata":"https://www.wikidata.org/wiki/Q2072214","display_name":"Personalized medicine","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C3018822202","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient data","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27300000190734863},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2662000060081482}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10462-026-11574-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-026-11574-x","pdf_url":null,"source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10462-026-11574-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-026-11574-x","pdf_url":null,"source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W3083403771","https://openalex.org/W3215776046","https://openalex.org/W4214758645","https://openalex.org/W4280499562","https://openalex.org/W4285121881","https://openalex.org/W4286488966","https://openalex.org/W4309680642","https://openalex.org/W4317498947","https://openalex.org/W4320732177","https://openalex.org/W4327522282","https://openalex.org/W4379745615","https://openalex.org/W4379982863","https://openalex.org/W4383345489","https://openalex.org/W4387003882","https://openalex.org/W4388568840","https://openalex.org/W4389893514","https://openalex.org/W4390480792","https://openalex.org/W4390970770","https://openalex.org/W4391695435","https://openalex.org/W4392281727","https://openalex.org/W4392543811","https://openalex.org/W4392699793","https://openalex.org/W4392796938","https://openalex.org/W4393033220","https://openalex.org/W4395027924","https://openalex.org/W4399168337","https://openalex.org/W4399435705","https://openalex.org/W4400410010","https://openalex.org/W4400411511","https://openalex.org/W4400679609","https://openalex.org/W4401541943","https://openalex.org/W4401664342","https://openalex.org/W4402006883","https://openalex.org/W4402220261","https://openalex.org/W4402401365","https://openalex.org/W4402762706","https://openalex.org/W4403863481","https://openalex.org/W4404026063","https://openalex.org/W4404350970","https://openalex.org/W4404377929","https://openalex.org/W4405024129","https://openalex.org/W4405026707","https://openalex.org/W4405284405","https://openalex.org/W4405335331","https://openalex.org/W4405993517","https://openalex.org/W4406163034","https://openalex.org/W4406259748","https://openalex.org/W4406445027","https://openalex.org/W4406457717","https://openalex.org/W4406461936","https://openalex.org/W4406558660","https://openalex.org/W4406610988","https://openalex.org/W4406747950","https://openalex.org/W4406973645","https://openalex.org/W4407410872","https://openalex.org/W4407416098","https://openalex.org/W4407637970","https://openalex.org/W4407693343","https://openalex.org/W4407773126","https://openalex.org/W4408008526","https://openalex.org/W4408222585","https://openalex.org/W4408733970","https://openalex.org/W4408793741","https://openalex.org/W4408996255","https://openalex.org/W4409124165","https://openalex.org/W4409373204","https://openalex.org/W4409480814","https://openalex.org/W4409481859","https://openalex.org/W4409581554","https://openalex.org/W4409831349","https://openalex.org/W4410593181","https://openalex.org/W4410780749","https://openalex.org/W4410907878","https://openalex.org/W4411688972","https://openalex.org/W4411846901","https://openalex.org/W4412521368","https://openalex.org/W4412948549","https://openalex.org/W4412971785","https://openalex.org/W4413147548","https://openalex.org/W4415626414"],"related_works":[],"abstract_inverted_index":{"Breast":[0],"cancer":[1],"(BC)":[2],"is":[3,13,119,189],"one":[4],"of":[5,34,52,81,89,96,112,117,171,178,196,225,247,269,279],"the":[6,29,50,79,87,94,110,115,169,176,179,185,192,241,245,265,277],"most":[7,186],"common":[8],"malignancies":[9],"among":[10],"women":[11],"and":[12,22,32,93,131,140,145,194,199,209,214,218,257,261,267,271],"associated":[14,120],"with":[15,121,203,212,221],"complex":[16],"mechanisms":[17],"in":[18,46,114,147,151,173,282],"cell":[19],"signalling":[20],"pathways":[21],"tumour":[23],"heterogeneity.":[24],"These":[25],"complexities":[26],"have":[27],"made":[28],"early":[30],"screening":[31,233],"selection":[33],"effective":[35],"treatments":[36],"a":[37,43],"significant":[38,187],"challenge.":[39],"Federated":[40],"Learning":[41],"(FL),":[42],"new":[44,273],"approach":[45],"artificial":[47],"intelligence,":[48],"enables":[49],"analysis":[51,177],"multicenter":[53],"data":[54,58,76,102,125,258],"without":[55],"requiring":[56],"physical":[57],"aggregation.":[59],"This":[60,164],"feature":[61],"leverages":[62],"privacy-preserving":[63],"mechanisms,":[64],"such":[65],"as":[66],"secure":[67],"aggregation,":[68],"to":[69,275],"improve":[70,86,232,276],"model":[71,138,149],"accuracy":[72,88,193],"while":[73],"preserving":[74],"patient":[75,249],"confidentiality.":[77],"In":[78,175],"field":[80,116],"BC,":[82],"FL":[83,113,172,230,270],"can":[84,231,254],"significantly":[85],"imaging-based":[90],"diagnostic":[91],"algorithms":[92],"efficiency":[95],"personalized":[97],"treatment":[98],"methods":[99],"by":[100,135,206],"integrating":[101],"from":[103],"multiple":[104],"medical":[105],"institutions.":[106],"Despite":[107],"these":[108],"advantages,":[109],"application":[111],"BC":[118],"specific":[122],"challenges,":[123],"including":[124],"heterogeneity":[126],"across":[127],"institutions,":[128],"high":[129],"computational":[130],"communication":[132,143],"overhead":[133],"caused":[134],"repeated":[136],"local":[137],"training":[139],"iterative":[141],"federated":[142],"rounds,":[144],"difficulty":[146],"ensuring":[148],"generalizability":[150],"diverse":[152],"clinical":[153,162,280],"settings,":[154],"which":[155],"pose":[156],"critical":[157],"practical":[158],"limitations":[159],"for":[160],"widespread":[161],"adoption.":[163],"review":[165],"article":[166],"systematically":[167],"examines":[168],"applications":[170],"BC.":[174,283],"articles,":[180],"it":[181],"was":[182],"found":[183],"that":[184,229],"focus":[188],"on":[190],"improving":[191],"precision":[195],"results,":[197],"Precision":[198],"Accuracy":[200],"Enhancement":[201],"(PAE)":[202],"29%,":[204],"followed":[205],"Data":[207],"Confidentiality":[208],"Protection":[210],"(DCP)":[211],"24%,":[213],"finally":[215],"Model":[216],"Robustness":[217],"Generalization":[219],"(MRG)":[220],"19%.":[222],"The":[223],"findings":[224],"this":[226,252],"study":[227],"indicate":[228],"accuracy,":[234],"recommend":[235],"more":[236],"targeted":[237],"treatments,":[238],"and,":[239],"at":[240],"same":[242],"time,":[243],"prevent":[244],"disclosure":[246],"sensitive":[248],"information.":[250],"Ultimately,":[251],"research":[253],"help":[255],"oncology":[256],"science":[259],"researchers":[260],"experts":[262],"better":[263],"understand":[264],"potential":[266],"challenges":[268],"develop":[272],"strategies":[274],"quality":[278],"care":[281]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-05-09T00:00:00"}