{"id":"https://openalex.org/W4312896266","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892284","title":"Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent","display_name":"Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312896266","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892284"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892284","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"https://openalex.org/A5009738158","display_name":"Mirko Polato","orcid":"https://orcid.org/0000-0003-4890-5020"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mirko Polato","raw_affiliation_strings":["University of Turin,Dept. of Computer Science,Turin,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turin,Dept. of Computer Science,Turin,Italy","institution_ids":["https://openalex.org/I55143463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101971371","display_name":"Roberto Esposito","orcid":"https://orcid.org/0000-0001-5366-292X"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Roberto Esposito","raw_affiliation_strings":["University of Turin,Dept. of Computer Science,Turin,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turin,Dept. of Computer Science,Turin,Italy","institution_ids":["https://openalex.org/I55143463"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089092418","display_name":"Marco Aldinucci","orcid":"https://orcid.org/0000-0001-8788-0829"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Aldinucci","raw_affiliation_strings":["University of Turin,Dept. of Computer Science,Turin,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turin,Dept. of Computer Science,Turin,Italy","institution_ids":["https://openalex.org/I55143463"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I55143463"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9843999743461609,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9814000129699707,"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":0.8100528717041016},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7339553236961365},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6457117795944214},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6373193860054016},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6358532905578613},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.6294814944267273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6126052737236023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5913923382759094},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.47199347615242004},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43029695749282837},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36056578159332275},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3400435447692871}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8100528717041016},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7339553236961365},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6457117795944214},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6373193860054016},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6358532905578613},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.6294814944267273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6126052737236023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5913923382759094},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.47199347615242004},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43029695749282837},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36056578159332275},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3400435447692871},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892284","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W150939725","https://openalex.org/W1500698297","https://openalex.org/W1925769220","https://openalex.org/W1968969471","https://openalex.org/W1988790447","https://openalex.org/W2097477220","https://openalex.org/W2145073242","https://openalex.org/W2397857137","https://openalex.org/W2493343568","https://openalex.org/W2783521339","https://openalex.org/W2963907629","https://openalex.org/W2995022099","https://openalex.org/W3004732066","https://openalex.org/W3049595782","https://openalex.org/W3086809868","https://openalex.org/W3101860164","https://openalex.org/W3110682008","https://openalex.org/W3118632980","https://openalex.org/W3120740533","https://openalex.org/W3133312630","https://openalex.org/W3164712068","https://openalex.org/W3198680619","https://openalex.org/W3206112800","https://openalex.org/W3206437044","https://openalex.org/W4200097921","https://openalex.org/W4206840430","https://openalex.org/W4212883601","https://openalex.org/W4285722492","https://openalex.org/W4287332481","https://openalex.org/W4318619660","https://openalex.org/W6640096974","https://openalex.org/W6681651645","https://openalex.org/W6728757088","https://openalex.org/W6738893770","https://openalex.org/W6790230083","https://openalex.org/W7036634323"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"Federated":[0,46],"Learning":[1,47],"has":[2,36,78],"been":[3,37,79],"proposed":[4],"to":[5,28,96,100,127],"develop":[6],"better":[7],"AI":[8],"systems":[9],"without":[10,112],"compromising":[11],"the":[12,18,50,64,67,70,124,145,161],"privacy":[13],"of":[14,21,53,66,123,154],"final":[15],"users":[16],"and":[17,56],"legitimate":[19],"interests":[20],"private":[22],"companies.":[23],"Initially":[24],"deployed":[25,38],"by":[26,61],"Google":[27],"predict":[29],"text":[30],"input":[31],"on":[32,114,144,156],"mobile":[33],"devices,":[34],"FL":[35,86,106],"in":[39,87,163],"many":[40],"other":[41,57,90],"industries.":[42],"Since":[43],"its":[44],"introduction,":[45],"mainly":[48],"exploited":[49],"inner":[51],"working":[52],"neural":[54],"networks":[55],"gradient":[58,115],"descent-based":[59,116],"algorithms":[60,107,162],"either":[62],"exchanging":[63],"weights":[65],"model":[68],"or":[69,98],"gradients":[71],"computed":[72],"during":[73],"learning.":[74],"While":[75],"this":[76],"approach":[77],"very":[80],"successful,":[81],"it":[82],"rules":[83],"out":[84],"applying":[85],"contexts":[88],"where":[89],"models":[91,111],"are":[92],"preferred,":[93],"e.g.,":[94],"easier":[95],"interpret":[97],"known":[99],"work":[101],"better.":[102],"This":[103],"paper":[104],"proposes":[105],"that":[108],"build":[109],"federated":[110,130],"relying":[113],"methods.":[117],"Specifically,":[118],"we":[119],"leverage":[120],"distributed":[121],"versions":[122],"AdaBoost":[125],"algorithm":[126],"acquire":[128],"strong":[129],"models.":[131,148],"In":[132],"contrast":[133],"with":[134],"previous":[135],"approaches,":[136],"our":[137],"proposal":[138],"does":[139],"not":[140],"put":[141],"any":[142],"constraint":[143],"client-side":[146],"learning":[147],"We":[149],"perform":[150],"a":[151],"large":[152],"set":[153],"experiments":[155],"ten":[157],"UCI":[158],"datasets,":[159],"comparing":[160],"six":[164],"non-iidness":[165],"settings.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
