{"id":"https://openalex.org/W4415125440","doi":"https://doi.org/10.1109/icmlt65785.2025.11193366","title":"Federated Learning in the Diagnosis of Medical Diseases: A Survey","display_name":"Federated Learning in the Diagnosis of Medical Diseases: A Survey","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4415125440","doi":"https://doi.org/10.1109/icmlt65785.2025.11193366"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193366","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Shuoshi Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuoshi Zhu","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yisheng Ruan","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yisheng Ruan","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107943977","display_name":"Tianyuan Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyuan Yu","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101717985","display_name":"Liang Bai","orcid":"https://orcid.org/0000-0002-2696-5885"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Bai","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24630287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"208","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9284999966621399,"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":0.9284999966621399,"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/leverage","display_name":"Leverage (statistics)","score":0.7613000273704529},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5331000089645386},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4909999966621399},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4740999937057495},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.4074999988079071},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.34619998931884766}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7613000273704529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6424999833106995},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5828999876976013},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193366","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2016795705","https://openalex.org/W2078524519","https://openalex.org/W2094218487","https://openalex.org/W2124260444","https://openalex.org/W2150534249","https://openalex.org/W2502810715","https://openalex.org/W2768181417","https://openalex.org/W2794825826","https://openalex.org/W2803487511","https://openalex.org/W2963946669","https://openalex.org/W3139487216","https://openalex.org/W4254373354","https://openalex.org/W4280619489","https://openalex.org/W4295037363","https://openalex.org/W4313889858","https://openalex.org/W4361289277","https://openalex.org/W4362654508","https://openalex.org/W4385565493","https://openalex.org/W4386365674","https://openalex.org/W4386959627","https://openalex.org/W4387068110","https://openalex.org/W4387211019","https://openalex.org/W4387211827","https://openalex.org/W4387225566","https://openalex.org/W4389438901","https://openalex.org/W4390872376","https://openalex.org/W4390970066","https://openalex.org/W4391129955","https://openalex.org/W4393305271","https://openalex.org/W4401132108","https://openalex.org/W4402727258","https://openalex.org/W4402809590","https://openalex.org/W4403069563","https://openalex.org/W4403391203","https://openalex.org/W4403826771","https://openalex.org/W4404567871","https://openalex.org/W4409094341"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,6,44,65,75,82,98,119,131,141],"advent":[2],"of":[3,8,46,68,77,85,100,121],"digital":[4],"healthcare,":[5],"volume":[7],"medical":[9,50,78,104],"imaging":[10],"data":[11,35,123],"has":[12],"grown":[13],"exponentially,":[14],"necessitating":[15],"advanced":[16],"analytics":[17],"for":[18],"improved":[19],"diagnostics":[20],"and":[21,71,112],"treatment":[22],"planning.":[23],"Federated":[24],"learning":[25,48,70],"(FL)":[26],"emerges":[27],"as":[28],"a":[29],"promising":[30],"solution":[31],"to":[32],"leverage":[33],"this":[34],"while":[36],"preserving":[37],"patient":[38,126],"privacy.":[39],"This":[40],"comprehensive":[41],"survey":[42,94],"explores":[43],"application":[45],"federated":[47,69],"in":[49,74,134,144],"image":[51,79],"analysis,":[52,80],"focusing":[53],"on":[54],"its":[55,72],"utility":[56],"across":[57,102],"various":[58],"disease":[59],"types.":[60],"We":[61,128],"begin":[62],"by":[63],"outlining":[64],"fundamental":[66],"principles":[67],"significance":[73],"context":[76],"emphasizing":[81],"privacy-preserving":[83],"nature":[84],"FL":[86,101,117,135],"that":[87],"aligns":[88],"with":[89],"stringent":[90],"healthcare":[91],"regulations.":[92],"The":[93,137],"then":[95],"delves":[96],"into":[97],"applications":[99],"different":[103],"domains,":[105],"including":[106],"Alzheimer\u2019s":[107],"Disease,":[108],"COVID-19,":[109],"skin":[110],"diseases,":[111],"Diabetic":[113],"Retinopathy,":[114],"highlighting":[115],"how":[116],"enables":[118],"analysis":[120],"decentralized":[122],"without":[124],"compromising":[125],"confidentiality.":[127],"also":[129],"discuss":[130],"datasets":[132],"used":[133],"research.":[136],"review":[138],"further":[139],"addresses":[140],"challenges":[142],"inherent":[143],"FL.":[145]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}