{"id":"https://openalex.org/W3109503640","doi":"https://doi.org/10.1145/3386367.3431678","title":"Combining split and federated architectures for efficiency and privacy in deep learning","display_name":"Combining split and federated architectures for efficiency and privacy in deep learning","publication_year":2020,"publication_date":"2020-11-23","ids":{"openalex":"https://openalex.org/W3109503640","doi":"https://doi.org/10.1145/3386367.3431678","mag":"3109503640"},"language":"en","primary_location":{"id":"doi:10.1145/3386367.3431678","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3386367.3431678","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3386367.3431678","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3386367.3431678","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016482968","display_name":"Valeria Turina","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121589","display_name":"UCLouvain Saint-Louis Brussels","ror":"https://ror.org/02ygek028","country_code":"BE","type":"education","lineage":["https://openalex.org/I4210121589","https://openalex.org/I95674353"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Valeria Turina","raw_affiliation_strings":["Saint Louis University"],"affiliations":[{"raw_affiliation_string":"Saint Louis University","institution_ids":["https://openalex.org/I4210121589"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034133274","display_name":"Zongshun Zhang","orcid":"https://orcid.org/0000-0002-8253-959X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zongshun Zhang","raw_affiliation_strings":["Boston University"],"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055238253","display_name":"Flavio Esposito","orcid":"https://orcid.org/0000-0002-7798-4584"},"institutions":[{"id":"https://openalex.org/I4210121589","display_name":"UCLouvain Saint-Louis Brussels","ror":"https://ror.org/02ygek028","country_code":"BE","type":"education","lineage":["https://openalex.org/I4210121589","https://openalex.org/I95674353"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Flavio Esposito","raw_affiliation_strings":["Saint Louis University"],"affiliations":[{"raw_affiliation_string":"Saint Louis University","institution_ids":["https://openalex.org/I4210121589"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044678468","display_name":"Ibrahim Matta","orcid":"https://orcid.org/0000-0003-4528-0344"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ibrahim Matta","raw_affiliation_strings":["Boston University"],"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016482968"],"corresponding_institution_ids":["https://openalex.org/I4210121589"],"apc_list":null,"apc_paid":null,"fwci":3.6703,"has_fulltext":true,"cited_by_count":37,"citation_normalized_percentile":{"value":0.94382739,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"562","last_page":"563"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T10237","display_name":"Cryptography and Data Security","score":0.9939000010490417,"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":0.979200005531311,"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":0.8677133321762085},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6977267861366272},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6142193078994751},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5696632862091064},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5634844303131104},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.5448799133300781},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5033909678459167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49390727281570435},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3891576826572418},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.38379693031311035}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8677133321762085},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6977267861366272},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6142193078994751},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5696632862091064},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5634844303131104},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.5448799133300781},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5033909678459167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49390727281570435},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3891576826572418},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.38379693031311035},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3386367.3431678","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3386367.3431678","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3386367.3431678","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3386367.3431678","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3386367.3431678","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3386367.3431678","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2032714365","display_name":null,"funder_award_id":"1908574","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G241445874","display_name":null,"funder_award_id":"CNS-1908677","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5946819335","display_name":null,"funder_award_id":"1836906","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6839420962","display_name":null,"funder_award_id":"1647084","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7688801239","display_name":null,"funder_award_id":"CNS-1908574","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8318512787","display_name":null,"funder_award_id":"CNS-1647084, CNS-1836906, CNS-1908574","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8468832381","display_name":null,"funder_award_id":"CNS-1836906","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3109503640.pdf","grobid_xml":"https://content.openalex.org/works/W3109503640.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2541884796","https://openalex.org/W2751343396","https://openalex.org/W2900319533","https://openalex.org/W2963209930","https://openalex.org/W2963390429","https://openalex.org/W3009048827","https://openalex.org/W3017855299"],"related_works":["https://openalex.org/W4317941881","https://openalex.org/W2998530156","https://openalex.org/W4323521275","https://openalex.org/W3035996294","https://openalex.org/W2954034773","https://openalex.org/W3013510494","https://openalex.org/W4229067761","https://openalex.org/W3091296419","https://openalex.org/W3145562225","https://openalex.org/W4322741898"],"abstract_inverted_index":{"Distributed":[0],"learning":[1,34,60],"systems":[2],"are":[3],"increasingly":[4],"being":[5],"adopted":[6],"for":[7],"a":[8],"variety":[9],"of":[10,57,66,76],"applications":[11],"as":[12],"centralized":[13],"training":[14],"becomes":[15],"unfeasible.":[16],"A":[17],"few":[18],"architectures":[19,61],"have":[20],"emerged":[21],"to":[22,30,72,84,97],"divide":[23],"and":[24,45,54,68,96,114],"conquer":[25],"the":[26,52,64,74,86,112],"computational":[27,87],"load,":[28],"or":[29,38],"run":[31],"privacy-aware":[32],"deep":[33],"models,":[35],"using":[36],"split":[37,67],"federated":[39,69],"learning.":[40],"Each":[41],"architecture":[42],"has":[43],"benefits":[44],"drawbacks.":[46],"In":[47,78],"this":[48],"work,":[49],"we":[50],"compare":[51],"efficiency":[53],"privacy":[55],"performance":[56],"two":[58],"distributed":[59],"that":[62,110],"combine":[63],"principles":[65],"learning,":[70],"trying":[71],"get":[73],"best":[75],"both.":[77],"particular,":[79],"our":[80,108],"design":[81],"goal":[82],"is":[83],"reduce":[85],"power":[88],"required":[89],"by":[90],"each":[91],"client":[92],"in":[93],"Federated":[94],"Learning":[95],"parallelize":[98],"Split":[99],"Learning.":[100],"We":[101],"share":[102],"some":[103],"initial":[104],"lessons":[105],"learned":[106],"from":[107],"implementation":[109],"leverages":[111],"PySyft":[113],"PyGrid":[115],"libraries.":[116]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
