{"id":"https://openalex.org/W4385482869","doi":"https://doi.org/10.1109/ijcnn54540.2023.10190999","title":"Federated Survival Forests","display_name":"Federated Survival Forests","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385482869","doi":"https://doi.org/10.1109/ijcnn54540.2023.10190999"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10190999","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10190999","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5088968519","display_name":"Alberto Archetti","orcid":"https://orcid.org/0000-0003-3826-4645"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Alberto Archetti","raw_affiliation_strings":["Politecnico di Milano,DEIB,Milan,Italy","DEIB, Politecnico di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Milano,DEIB,Milan,Italy","institution_ids":["https://openalex.org/I93860229"]},{"raw_affiliation_string":"DEIB, Politecnico di Milano, Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003932703","display_name":"Matteo Matteucci","orcid":"https://orcid.org/0000-0002-8306-6739"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Matteo Matteucci","raw_affiliation_strings":["Politecnico di Milano,DEIB,Milan,Italy","DEIB, Politecnico di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Milano,DEIB,Milan,Italy","institution_ids":["https://openalex.org/I93860229"]},{"raw_affiliation_string":"DEIB, Politecnico di Milano, Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088968519"],"corresponding_institution_ids":["https://openalex.org/I93860229"],"apc_list":null,"apc_paid":null,"fwci":2.0979,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.89591591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9025999903678894,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/computer-science","display_name":"Computer science","score":0.8302258253097534},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5777701139450073},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.577098548412323},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5723773241043091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5169881582260132},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.501793622970581},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.416043221950531},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3218306303024292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8302258253097534},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5777701139450073},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.577098548412323},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5723773241043091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5169881582260132},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.501793622970581},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.416043221950531},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3218306303024292},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10190999","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10190999","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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":0.6000000238418579,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W128574488","https://openalex.org/W1946622722","https://openalex.org/W1986546598","https://openalex.org/W2052825782","https://openalex.org/W2085107973","https://openalex.org/W2106147539","https://openalex.org/W2109426455","https://openalex.org/W2120579447","https://openalex.org/W2167205245","https://openalex.org/W2236833089","https://openalex.org/W2753919178","https://openalex.org/W2783134495","https://openalex.org/W2789172526","https://openalex.org/W2798890825","https://openalex.org/W2954512644","https://openalex.org/W2963232127","https://openalex.org/W2972570881","https://openalex.org/W2995022099","https://openalex.org/W3006555759","https://openalex.org/W3012501605","https://openalex.org/W3017915781","https://openalex.org/W3021654819","https://openalex.org/W3037491963","https://openalex.org/W3038022836","https://openalex.org/W3039226036","https://openalex.org/W3041047318","https://openalex.org/W3086590218","https://openalex.org/W3097349486","https://openalex.org/W3147894994","https://openalex.org/W3178336997","https://openalex.org/W3202929943","https://openalex.org/W3206144059","https://openalex.org/W3213815372","https://openalex.org/W3217644914","https://openalex.org/W4210284152","https://openalex.org/W4214512856","https://openalex.org/W4220688658","https://openalex.org/W4226204583","https://openalex.org/W4233019352","https://openalex.org/W4281647175","https://openalex.org/W4285429141","https://openalex.org/W4287332481","https://openalex.org/W4287756296","https://openalex.org/W4289147229","https://openalex.org/W4292967441","https://openalex.org/W4293371134","https://openalex.org/W4293812156","https://openalex.org/W4297957988","https://openalex.org/W4299283926","https://openalex.org/W4302024112","https://openalex.org/W4302306698","https://openalex.org/W4310263890","https://openalex.org/W4310422013","https://openalex.org/W4312517097","https://openalex.org/W4318619660","https://openalex.org/W4320719463","https://openalex.org/W4362490016","https://openalex.org/W4376852252","https://openalex.org/W6728757088","https://openalex.org/W6759238902","https://openalex.org/W6773817997","https://openalex.org/W6785337799","https://openalex.org/W6811338132"],"related_works":["https://openalex.org/W4308191010","https://openalex.org/W4323021782","https://openalex.org/W2911455822","https://openalex.org/W3174196512","https://openalex.org/W4318350883","https://openalex.org/W4293525103","https://openalex.org/W3211546796","https://openalex.org/W4281616679","https://openalex.org/W4281560664","https://openalex.org/W3018959556"],"abstract_inverted_index":{"Survival":[0,22,137],"analysis":[1,23,58,117],"is":[2],"a":[3,14,20,63,83,111,141,147],"subfield":[4],"of":[5,13,17,56,65,92,121,158,167,177],"statistics":[6],"concerned":[7],"with":[8,192,199,207,224],"modeling":[9],"the":[10,54,89,122,127,133,165],"occurrence":[11],"time":[12],"particular":[15],"event":[16],"interest":[18],"for":[19,115],"population.":[21],"found":[24],"widespread":[25,90],"applications":[26,35],"in":[27,95,188,211,221],"healthcare,":[28],"engineering,":[29],"and":[30,44,174,180],"social":[31],"sciences.":[32],"However,":[33,87],"real-world":[34,189],"involve":[36],"survival":[37,57,104,116,125,129,209],"datasets":[38,76,195],"that":[39],"are":[40,185],"distributed,":[41],"incomplete,":[42],"censored,":[43],"confidential.":[45],"In":[46,106],"this":[47,107],"context,":[48],"federated":[49,93,103,113,153,159,190,212,219],"learning":[50,61,72,94],"can":[51],"tremendously":[52],"improve":[53],"performance":[55],"applications.":[59],"Federated":[60,136],"provides":[62],"set":[64],"privacy-preserving":[66],"techniques":[67],"to":[68,82,151],"jointly":[69],"train":[70],"machine":[71],"models":[73,154,210],"on":[74,102,119,197],"multiple":[75,193],"without":[77],"compromising":[78],"user":[79],"privacy,":[80],"leading":[81],"better":[84],"generalization":[85],"performance.":[86],"despite":[88],"development":[91],"recent":[96],"AI":[97],"research,":[98],"few":[99],"studies":[100],"focus":[101],"analysis.":[105],"work,":[108],"we":[109],"present":[110],"novel":[112],"algorithm":[114],"based":[118],"one":[120],"most":[123],"successful":[124],"models,":[126],"random":[128,168],"forest.":[130],"We":[131],"call":[132],"proposed":[134],"method":[135],"Forest":[138],"(FedSurF).":[139],"With":[140],"single":[142],"communication":[143],"round,":[144],"FedSurF":[145,162,206,216],"obtains":[146],"discriminative":[148],"power":[149],"comparable":[150],"deep-learning-based":[152,218],"trained":[155],"over":[156],"hundreds":[157],"iterations.":[160],"Moreover,":[161],"retains":[163],"all":[164],"advantages":[166,184],"forests,":[169],"namely":[170],"low":[171,200],"computational":[172,201],"cost":[173],"natural":[175],"handling":[176],"missing":[178],"values":[179],"incomplete":[181],"datasets.":[182],"These":[183],"especially":[186],"desirable":[187],"environments":[191,223],"small":[194],"stored":[196],"devices":[198],"capabilities.":[202],"Numerical":[203],"experiments":[204],"compare":[205],"state-of-the-art":[208],"networks,":[213],"showing":[214],"how":[215],"outperforms":[217],"algorithms":[220],"realistic":[222],"non-identically":[225],"distributed":[226],"data.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}