{"id":"https://openalex.org/W4226094809","doi":"https://doi.org/10.1109/bigdata52589.2021.9671583","title":"Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning","display_name":"Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4226094809","doi":"https://doi.org/10.1109/bigdata52589.2021.9671583"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671583","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","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/A5001308300","display_name":"Yusen Wu","orcid":"https://orcid.org/0000-0003-4299-6945"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yusen Wu","raw_affiliation_strings":["University of Maryland, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353673","display_name":"Hao Chen","orcid":"https://orcid.org/0009-0001-6480-7976"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Chen","raw_affiliation_strings":["University of Maryland, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327905","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0002-1665-8398"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["University of Maryland, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101772711","display_name":"Chao Liu","orcid":"https://orcid.org/0000-0002-3971-3919"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Liu","raw_affiliation_strings":["University of Maryland, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108059455","display_name":"Phuong Nguyen","orcid":"https://orcid.org/0000-0002-4862-8396"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Phuong Nguyen","raw_affiliation_strings":["OpenKneck Inc, Halethorpe, MD, USA","University of Maryland, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"OpenKneck Inc, Halethorpe, MD, USA","institution_ids":[]},{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072869372","display_name":"Yelena Yesha","orcid":null},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]},{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yelena Yesha","raw_affiliation_strings":["University of Maryland, Baltimore, MD, USA","University of Miami, FL, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"University of Miami, FL, USA","institution_ids":["https://openalex.org/I145608581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5001308300"],"corresponding_institution_ids":["https://openalex.org/I126744593"],"apc_list":null,"apc_paid":null,"fwci":1.54,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.82707141,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3380","last_page":"3389"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10772","display_name":"Distributed systems and fault tolerance","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10772","display_name":"Distributed systems and fault tolerance","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9923999905586243,"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":0.7755804061889648},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7687636613845825},{"id":"https://openalex.org/keywords/byzantine-fault-tolerance","display_name":"Byzantine fault tolerance","score":0.7429605722427368},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5948500633239746},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.5815585851669312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.579305112361908},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.5067866444587708},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4649336040019989},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4338240623474121},{"id":"https://openalex.org/keywords/quantum-byzantine-agreement","display_name":"Quantum Byzantine agreement","score":0.4270651042461395},{"id":"https://openalex.org/keywords/byzantine-architecture","display_name":"Byzantine architecture","score":0.4207976162433624},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.41548076272010803},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.360409677028656},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3492752015590668},{"id":"https://openalex.org/keywords/fault-tolerance","display_name":"Fault tolerance","score":0.2320612370967865}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7755804061889648},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7687636613845825},{"id":"https://openalex.org/C168021876","wikidata":"https://www.wikidata.org/wiki/Q1353446","display_name":"Byzantine fault tolerance","level":3,"score":0.7429605722427368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5948500633239746},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.5815585851669312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.579305112361908},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.5067866444587708},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4649336040019989},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4338240623474121},{"id":"https://openalex.org/C17532199","wikidata":"https://www.wikidata.org/wiki/Q17083590","display_name":"Quantum Byzantine agreement","level":4,"score":0.4270651042461395},{"id":"https://openalex.org/C104562893","wikidata":"https://www.wikidata.org/wiki/Q47591","display_name":"Byzantine architecture","level":2,"score":0.4207976162433624},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.41548076272010803},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.360409677028656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3492752015590668},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.2320612370967865},{"id":"https://openalex.org/C195244886","wikidata":"https://www.wikidata.org/wiki/Q41493","display_name":"Ancient history","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671583","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W2003214215","https://openalex.org/W2089138728","https://openalex.org/W2110880385","https://openalex.org/W2112796928","https://openalex.org/W2114579022","https://openalex.org/W2138243089","https://openalex.org/W2156580773","https://openalex.org/W2173213060","https://openalex.org/W2189465200","https://openalex.org/W2481170768","https://openalex.org/W2534313446","https://openalex.org/W2752689052","https://openalex.org/W2765407302","https://openalex.org/W2769644379","https://openalex.org/W2788308444","https://openalex.org/W2788816110","https://openalex.org/W2789911054","https://openalex.org/W2899771611","https://openalex.org/W2944844562","https://openalex.org/W2949384269","https://openalex.org/W2950447132","https://openalex.org/W2953384591","https://openalex.org/W2964261056","https://openalex.org/W2979625852","https://openalex.org/W2981860227","https://openalex.org/W3046556863","https://openalex.org/W3048715803","https://openalex.org/W3100529187","https://openalex.org/W3118608800","https://openalex.org/W4288414371","https://openalex.org/W4294106961","https://openalex.org/W4301239768","https://openalex.org/W4318619660","https://openalex.org/W6682904396","https://openalex.org/W6687322159","https://openalex.org/W6713134421","https://openalex.org/W6728757088","https://openalex.org/W6743821447","https://openalex.org/W6745136726","https://openalex.org/W6746200960","https://openalex.org/W6748268456","https://openalex.org/W6748645090","https://openalex.org/W6748786018","https://openalex.org/W6748805329","https://openalex.org/W6753124209","https://openalex.org/W6754358263","https://openalex.org/W6756040250","https://openalex.org/W6762375199","https://openalex.org/W6762462524","https://openalex.org/W6764309476","https://openalex.org/W6768324076","https://openalex.org/W6769096552","https://openalex.org/W6770634426","https://openalex.org/W6771536673","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W1698117324","https://openalex.org/W2292106967","https://openalex.org/W4290996430","https://openalex.org/W4210453042","https://openalex.org/W2997526278","https://openalex.org/W2109232026","https://openalex.org/W2775054947","https://openalex.org/W2146384899","https://openalex.org/W603186994","https://openalex.org/W2953763514"],"abstract_inverted_index":{"Adversarial":[0],"attacks":[1,94,110],"attempt":[2,35],"to":[3,36,67],"disrupt":[4],"the":[5,38,48,72,101,112,120,152,162],"training,":[6],"retraining,":[7],"and":[8,14,115],"utilizing":[9],"of":[10,103,151,165],"artificial":[11],"intelligence":[12],"(AI)":[13],"machine":[15,21],"learning":[16,22],"models":[17,114,130],"in":[18,61,167],"large-scale":[19],"distributed":[20,62,82],"systems.":[23],"This":[24],"causes":[25],"security":[26],"risks":[27],"on":[28,71],"its":[29],"prediction":[30,73],"outcome.":[31,74],"For":[32],"example,":[33],"attackers":[34],"poison":[37],"model":[39],"by":[40],"either":[41],"presenting":[42],"inaccurate":[43],"misrepresentative":[44],"data":[45],"or":[46,157],"altering":[47],"models'":[49],"parameters.":[50],"In":[51,75],"addition,":[52],"Byzantine":[53,97,117],"faults":[54],"including":[55],"software,":[56],"hardware,":[57],"network":[58],"issues":[59],"occur":[60],"systems":[63],"which":[64,91],"also":[65],"lead":[66],"a":[68,80,116],"negative":[69],"impact":[70],"this":[76],"paper,":[77],"we":[78],"propose":[79],"novel":[81],"training":[83,121],"algorithm,":[84],"partial":[85],"synchronous":[86],"stochastic":[87],"gradient":[88],"descent":[89],"(ParSGD),":[90],"defends":[92],"adversarial":[93,109],"and/or":[95],"tolerates":[96],"faults.":[98],"We":[99,159],"demonstrate":[100],"effectiveness":[102],"our":[104],"algorithm":[105],"under":[106],"three":[107],"common":[108],"again":[111],"ML":[113,129],"fault":[118],"during":[119],"phase.":[122],"Our":[123],"results":[124],"show":[125],"that":[126],"using":[127],"ParSGD,":[128],"can":[131],"still":[132],"produce":[133],"accurate":[134],"predictions":[135],"as":[136],"if":[137],"it":[138],"is":[139],"not":[140],"being":[141,155],"attacked":[142],"nor":[143],"having":[144],"failures":[145],"at":[146],"all":[147],"when":[148],"almost":[149],"half":[150],"nodes":[153],"are":[154],"compromised":[156],"failed.":[158],"will":[160],"report":[161],"experimental":[163],"evaluations":[164],"ParSGD":[166],"comparison":[168],"with":[169],"other":[170],"algorithms.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}