{"id":"https://openalex.org/W2963334472","doi":"https://doi.org/10.1609/aaai.v33i01.33011544","title":"RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets","display_name":"RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2963334472","doi":"https://doi.org/10.1609/aaai.v33i01.33011544","mag":"2963334472"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33011544","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33011544","pdf_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3968/3846","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3968/3846","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100379663","display_name":"Liping Li","orcid":"https://orcid.org/0000-0001-9876-5324"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liping Li","raw_affiliation_strings":["University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101685540","display_name":"Wei Xu","orcid":"https://orcid.org/0000-0003-2914-9039"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Xu","raw_affiliation_strings":["University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100783476","display_name":"Tianyi Chen","orcid":"https://orcid.org/0000-0003-3477-1439"},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Chen","raw_affiliation_strings":["University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026758314","display_name":"Georgios B. Giannakis","orcid":"https://orcid.org/0000-0002-0196-0260"},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgios B. Giannakis","raw_affiliation_strings":["University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029820392","display_name":"Qing Ling","orcid":"https://orcid.org/0000-0003-4222-5964"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qing Ling","raw_affiliation_strings":["Sun Yat-Sen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100379663"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":19.8151,"has_fulltext":true,"cited_by_count":361,"citation_normalized_percentile":{"value":0.99324553,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"33","issue":"01","first_page":"1544","last_page":"1551"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9997000098228455,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9997000098228455,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9986000061035156,"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/T12676","display_name":"Machine Learning and ELM","score":0.9952999949455261,"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/subgradient-method","display_name":"Subgradient method","score":0.8780993223190308},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7305144667625427},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.5916821956634521},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5611645579338074},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4903848171234131},{"id":"https://openalex.org/keywords/byzantine-fault-tolerance","display_name":"Byzantine fault tolerance","score":0.4575958847999573},{"id":"https://openalex.org/keywords/quantum-byzantine-agreement","display_name":"Quantum Byzantine agreement","score":0.45629382133483887},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3362480401992798},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33345723152160645},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.2866120934486389},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2560531497001648},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.19378694891929626},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18673783540725708},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1344263255596161},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07425978779792786}],"concepts":[{"id":"https://openalex.org/C158968445","wikidata":"https://www.wikidata.org/wiki/Q7631150","display_name":"Subgradient method","level":2,"score":0.8780993223190308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7305144667625427},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.5916821956634521},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5611645579338074},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4903848171234131},{"id":"https://openalex.org/C168021876","wikidata":"https://www.wikidata.org/wiki/Q1353446","display_name":"Byzantine fault tolerance","level":3,"score":0.4575958847999573},{"id":"https://openalex.org/C17532199","wikidata":"https://www.wikidata.org/wiki/Q17083590","display_name":"Quantum Byzantine agreement","level":4,"score":0.45629382133483887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3362480401992798},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33345723152160645},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2866120934486389},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2560531497001648},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.19378694891929626},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18673783540725708},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1344263255596161},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07425978779792786},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v33i01.33011544","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33011544","pdf_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3968/3846","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33011544","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33011544","pdf_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3968/3846","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1515802232","display_name":null,"funder_award_id":"1711471","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4963119340","display_name":null,"funder_award_id":"1500713","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963334472.pdf","grobid_xml":"https://content.openalex.org/works/W2963334472.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1992208280","https://openalex.org/W2104927807","https://openalex.org/W2123705108","https://openalex.org/W2127941149","https://openalex.org/W2161669108","https://openalex.org/W2403871130","https://openalex.org/W2752689052","https://openalex.org/W2783291400","https://openalex.org/W2788308444","https://openalex.org/W2789903762","https://openalex.org/W2789911054","https://openalex.org/W2798551148","https://openalex.org/W2804165311","https://openalex.org/W2804948787","https://openalex.org/W2810917741","https://openalex.org/W2887439430","https://openalex.org/W2952782294","https://openalex.org/W2963334472","https://openalex.org/W2963495269","https://openalex.org/W2963773265","https://openalex.org/W2964261056","https://openalex.org/W3106753174","https://openalex.org/W4234117503","https://openalex.org/W4292084264","https://openalex.org/W4300427714","https://openalex.org/W6738383168","https://openalex.org/W7056210342"],"related_works":["https://openalex.org/W1698117324","https://openalex.org/W2292106967","https://openalex.org/W4290996430","https://openalex.org/W1556217901","https://openalex.org/W4210453042","https://openalex.org/W2997526278","https://openalex.org/W2109232026","https://openalex.org/W2775054947","https://openalex.org/W2146384899","https://openalex.org/W2953763514"],"abstract_inverted_index":{"In":[0,103],"this":[1],"paper,":[2],"we":[3,140],"propose":[4],"a":[5,64,134,147,199],"class":[6,136],"of":[7,20,24,84,107,137,158,165,175,184,196],"robust":[8],"stochastic":[9,177],"subgradient":[10],"methods":[11,62],"for":[12,133],"distributed":[13,125],"learning":[14,32,77,152],"from":[15],"heterogeneous":[16],"datasets":[17],"at":[18],"presence":[19],"an":[21],"unknown":[22],"number":[23,157],"Byzantine":[25,28,85,159,168,185],"workers.":[26],"The":[27,57,87],"workers,":[29,129],"during":[30],"the":[31,40,54,60,69,76,81,108,116,119,128,151,156,162,171,176,193,204],"process,":[33],"may":[34],"send":[35],"arbitrary":[36],"incorrect":[37],"messages":[38],"to":[39,43,59,74,105,146,203],"master":[41],"due":[42],"data":[44,120],"corruptions,":[45],"communication":[46],"failures":[47],"or":[48],"malicious":[49],"attacks,":[50],"and":[51,79,123,130,198],"consequently":[52],"bias":[53],"learned":[55],"model.":[56],"key":[58],"proposed":[61],"is":[63,170,182],"regularization":[65],"term":[66],"incorporated":[67],"with":[68,150],"objective":[70],"function":[71],"so":[72],"as":[73,173],"robustify":[75],"task":[78],"mitigate":[80],"negative":[82],"effects":[83],"attacks.":[86,186],"resultant":[88],"subgradient-based":[89],"algorithms":[90],"are":[91,121],"termed":[92],"Byzantine-Robust":[93],"Stochastic":[94],"Aggregation":[95],"methods,":[96],"justifying":[97],"our":[98],"acronym":[99],"RSA":[100,111,144,166,197],"used":[101],"henceforth.":[102],"contrast":[104],"most":[106],"existing":[109],"algorithms,":[110],"does":[112],"not":[113],"rely":[114],"on":[115,127,155,189],"assumption":[117],"that":[118,174],"independent":[122],"identically":[124],"(i.i.d.)":[126],"hence":[131],"fits":[132],"wider":[135],"applications.":[138],"Theoretically,":[139],"show":[141],"that:":[142],"i)":[143],"converges":[145],"near-optimal":[148],"solution":[149],"error":[153],"dependent":[154],"workers;":[160],"ii)":[161],"convergence":[163],"rate":[164],"under":[167],"attacks":[169],"same":[172],"gradient":[178],"descent":[179],"method,":[180],"which":[181],"free":[183],"Numerically,":[187],"experiments":[188],"real":[190],"dataset":[191],"corroborate":[192],"competitive":[194],"performance":[195],"complexity":[200],"reduction":[201],"compared":[202],"state-of-the-art":[205],"alternatives.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":70},{"year":2024,"cited_by_count":83},{"year":2023,"cited_by_count":55},{"year":2022,"cited_by_count":49},{"year":2021,"cited_by_count":49},{"year":2020,"cited_by_count":41},{"year":2019,"cited_by_count":10}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
