{"id":"https://openalex.org/W4375798903","doi":"https://doi.org/10.1109/tpds.2023.3273490","title":"Securing Distributed SGD Against Gradient Leakage Threats","display_name":"Securing Distributed SGD Against Gradient Leakage Threats","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4375798903","doi":"https://doi.org/10.1109/tpds.2023.3273490"},"language":"en","primary_location":{"id":"doi:10.1109/tpds.2023.3273490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpds.2023.3273490","pdf_url":null,"source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Parallel and Distributed Systems","raw_type":"journal-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/A5069331320","display_name":"Wenqi Wei","orcid":"https://orcid.org/0000-0001-9177-114X"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenqi Wei","raw_affiliation_strings":["Computer and Information Science Department, Fordham University, New York City, NY, USA","School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Computer and Information Science Department, Fordham University, New York City, NY, USA","institution_ids":["https://openalex.org/I164389053"]},{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343991","display_name":"Ling Liu","orcid":"https://orcid.org/0000-0002-4138-3082"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ling Liu","raw_affiliation_strings":["School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102728193","display_name":"Jingya Zhou","orcid":"https://orcid.org/0000-0003-0721-7424"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingya Zhou","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028240524","display_name":"Ka-Ho Chow","orcid":"https://orcid.org/0000-0001-5917-2577"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ka-Ho Chow","raw_affiliation_strings":["School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060093535","display_name":"Yanzhao Wu","orcid":"https://orcid.org/0000-0001-8761-5486"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanzhao Wu","raw_affiliation_strings":["School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5069331320"],"corresponding_institution_ids":["https://openalex.org/I130701444","https://openalex.org/I164389053"],"apc_list":null,"apc_paid":null,"fwci":4.871,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.960743,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"34","issue":"7","first_page":"2040","last_page":"2054"},"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/T10237","display_name":"Cryptography and Data Security","score":0.9851999878883362,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9843000173568726,"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/differential-privacy","display_name":"Differential privacy","score":0.8206725120544434},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7637705206871033},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5977376699447632},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5971096754074097},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5226430892944336},{"id":"https://openalex.org/keywords/information-leakage","display_name":"Information leakage","score":0.49334803223609924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4563392698764801},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.43975555896759033},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.42388927936553955},{"id":"https://openalex.org/keywords/artificial-noise","display_name":"Artificial noise","score":0.41512390971183777},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41015422344207764},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3853864073753357},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35049736499786377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3345686197280884},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.27353084087371826},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.23989322781562805},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19453927874565125},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.16568931937217712},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.08316901326179504}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8206725120544434},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7637705206871033},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5977376699447632},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5971096754074097},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5226430892944336},{"id":"https://openalex.org/C2779201187","wikidata":"https://www.wikidata.org/wiki/Q2775060","display_name":"Information leakage","level":2,"score":0.49334803223609924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4563392698764801},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.43975555896759033},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.42388927936553955},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.41512390971183777},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41015422344207764},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3853864073753357},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35049736499786377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3345686197280884},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.27353084087371826},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.23989322781562805},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19453927874565125},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.16568931937217712},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.08316901326179504},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpds.2023.3273490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpds.2023.3273490","pdf_url":null,"source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Parallel and Distributed Systems","raw_type":"journal-article"},{"id":"pmh:oai:hub.hku.hk:10722/343425","is_oa":false,"landing_page_url":"https://hub.hku.hk/handle/10722/343425","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G634024503","display_name":null,"funder_award_id":"NSF 2038029","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G7266013470","display_name":null,"funder_award_id":"NSF 1564097","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"}],"funders":[{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W2051267297","https://openalex.org/W2053637704","https://openalex.org/W2096870293","https://openalex.org/W2132172731","https://openalex.org/W2167372639","https://openalex.org/W2245160765","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2535838896","https://openalex.org/W2767079719","https://openalex.org/W2774000609","https://openalex.org/W2781091734","https://openalex.org/W2784621220","https://openalex.org/W2785361959","https://openalex.org/W2804268522","https://openalex.org/W2810715221","https://openalex.org/W2903389359","https://openalex.org/W2912023992","https://openalex.org/W2927692314","https://openalex.org/W2950943617","https://openalex.org/W2951696358","https://openalex.org/W2963456518","https://openalex.org/W2964162474","https://openalex.org/W2969261410","https://openalex.org/W2970408908","https://openalex.org/W2981759742","https://openalex.org/W3000479830","https://openalex.org/W3007844454","https://openalex.org/W3016632787","https://openalex.org/W3027749727","https://openalex.org/W3085804918","https://openalex.org/W3102360395","https://openalex.org/W3132138018","https://openalex.org/W3175192640","https://openalex.org/W3176786489","https://openalex.org/W3192324887","https://openalex.org/W3203780767","https://openalex.org/W3213758553","https://openalex.org/W4205228770","https://openalex.org/W4285555619","https://openalex.org/W4287822453","https://openalex.org/W4288117700","https://openalex.org/W4288358239","https://openalex.org/W4297687186","https://openalex.org/W4298221930","https://openalex.org/W4318619660","https://openalex.org/W6663928093","https://openalex.org/W6677855611","https://openalex.org/W6728757088","https://openalex.org/W6737496325","https://openalex.org/W6746720608","https://openalex.org/W6746839373","https://openalex.org/W6747732332","https://openalex.org/W6747855403","https://openalex.org/W6751754709","https://openalex.org/W6752985224","https://openalex.org/W6756699189","https://openalex.org/W6763202302","https://openalex.org/W6764838729","https://openalex.org/W6767236195","https://openalex.org/W6773039429","https://openalex.org/W6775078712","https://openalex.org/W6775563089","https://openalex.org/W6784311791","https://openalex.org/W6803588526","https://openalex.org/W6803951002"],"related_works":["https://openalex.org/W3038283795","https://openalex.org/W4401550921","https://openalex.org/W4361791424","https://openalex.org/W4296973715","https://openalex.org/W3093310219","https://openalex.org/W4387193529","https://openalex.org/W3101646702","https://openalex.org/W2022893347","https://openalex.org/W2047759772","https://openalex.org/W4375869362"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,76],"holistic":[4],"approach":[5,80,111,168],"to":[6,81],"gradient":[7,30,40,77,119,165,186],"leakage":[8,78,166,187],"resilient":[9,79,167],"distributed":[10,83],"Stochastic":[11],"Gradient":[12],"Descent":[13],"(SGD).":[14],"<italic":[15,70,133],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[16,71,134],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">First</i>":[17],",":[18,73,136],"we":[19,74,137],"analyze":[20,51],"two":[21],"types":[22],"of":[23,55,114,117,152],"strategies":[24],"for":[25],"privacy-enhanced":[26],"federated":[27,86,102,130],"learning:":[28],"(i)":[29],"pruning":[31],"with":[32,42,88,99,174],"random":[33,44],"selection":[34],"or":[35,46],"low-rank":[36],"filtering":[37],"and":[38,58,67,105,149,182],"(ii)":[39],"perturbation":[41],"additive":[43],"noise":[45,92,103,107,124],"differential":[47,89,179],"privacy":[48,63,90,141,145,180],"noise.":[49],"We":[50],"the":[52,94,100,115,129,144,153,171],"inherent":[53],"limitations":[54],"these":[56],"approaches":[57],"their":[59],"underlying":[60],"impact":[61],"on":[62,143],"guarantee,":[64,146,181],"model":[65,131,147],"accuracy,":[66],"attack":[68,150],"resilience.":[69],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Next</i>":[72],"present":[75],"securing":[82],"SGD":[84],"in":[85],"learning,":[87],"controlled":[91],"as":[93],"tool.":[95],"Unlike":[96],"conventional":[97],"methods":[98,173],"per-client":[101],"injection":[104,125],"fixed":[106],"parameter":[108],"strategy,":[109],"our":[110,164],"keeps":[112],"track":[113],"trend":[116],"per-example":[118],"updates.":[120],"It":[121],"makes":[122],"adaptive":[123],"closely":[126],"aligned":[127],"throughout":[128],"training.":[132],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Finally</i>":[135],"provide":[138],"an":[139],"empirical":[140],"analysis":[142],"utility,":[148],"resilience":[151,184],"proposed":[154],"approach.":[155],"Extensive":[156],"evaluation":[157],"using":[158],"five":[159],"benchmark":[160],"datasets":[161],"demonstrates":[162],"that":[163],"can":[169],"outperform":[170],"state-of-the-art":[172],"competitive":[175],"accuracy":[176],"performance,":[177],"strong":[178],"high":[183],"against":[185],"attacks.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}