{"id":"https://openalex.org/W3093809760","doi":"https://doi.org/10.1109/mlsp49062.2020.9231531","title":"Secure Federated Averaging Algorithm with Differential Privacy","display_name":"Secure Federated Averaging Algorithm with Differential Privacy","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3093809760","doi":"https://doi.org/10.1109/mlsp49062.2020.9231531","mag":"3093809760"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp49062.2020.9231531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5100633409","display_name":"Yiwei Li","orcid":"https://orcid.org/0000-0002-8380-4164"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yiwei Li","raw_affiliation_strings":["Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064271996","display_name":"Tsung\u2010Hui Chang","orcid":"https://orcid.org/0000-0003-1349-2764"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tsung-Hui Chang","raw_affiliation_strings":["School of Science and Engineering, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586","https://openalex.org/I4210116924"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061813280","display_name":"Chong\u2010Yung Chi","orcid":"https://orcid.org/0000-0001-5004-7155"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chong-Yung Chi","raw_affiliation_strings":["Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.994700014591217,"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":0.9908999800682068,"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":0.8990960121154785},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8132548928260803},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6436285972595215},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5902393460273743},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5807719230651855},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48121312260627747},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.4631601572036743},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.448653906583786},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4398255944252014},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.41717129945755005},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3558673858642578},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24012598395347595},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.22481754422187805},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1347028613090515},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12708774209022522}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8990960121154785},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8132548928260803},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6436285972595215},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5902393460273743},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5807719230651855},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48121312260627747},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.4631601572036743},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.448653906583786},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4398255944252014},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.41717129945755005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3558673858642578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24012598395347595},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.22481754422187805},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1347028613090515},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12708774209022522},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp49062.2020.9231531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1557833142","https://openalex.org/W1992926795","https://openalex.org/W2027595342","https://openalex.org/W2051267297","https://openalex.org/W2084812512","https://openalex.org/W2166706236","https://openalex.org/W2473418344","https://openalex.org/W2541884796","https://openalex.org/W2803867449","https://openalex.org/W2810065831","https://openalex.org/W2810715221","https://openalex.org/W2890924858","https://openalex.org/W2955213239","https://openalex.org/W2962741900","https://openalex.org/W2963179579","https://openalex.org/W2963924077","https://openalex.org/W2981981504","https://openalex.org/W2982720039","https://openalex.org/W2987714677","https://openalex.org/W2990239018","https://openalex.org/W2991308345","https://openalex.org/W2995653155","https://openalex.org/W3043758338","https://openalex.org/W3101329503","https://openalex.org/W4205228770","https://openalex.org/W4294106961","https://openalex.org/W4318619660","https://openalex.org/W6684249991","https://openalex.org/W6752985224","https://openalex.org/W6754416507"],"related_works":["https://openalex.org/W3038283795","https://openalex.org/W2604501336","https://openalex.org/W2734500670","https://openalex.org/W2558166297","https://openalex.org/W4361791424","https://openalex.org/W3002546633","https://openalex.org/W2964170259","https://openalex.org/W2765682467","https://openalex.org/W4206119629","https://openalex.org/W4382937879"],"abstract_inverted_index":{"Federated":[0],"learning":[1,14],"(FL),":[2],"as":[3],"a":[4,15],"recent":[5],"advance":[6],"of":[7,13,105,142,148],"distributed":[8],"machine":[9],"learning,":[10],"is":[11,101],"capable":[12],"model":[16,113],"over":[17],"the":[18,23,28,45,55,66,70,77,89,102,125,135,140,143],"network":[19],"without":[20],"directly":[21],"accessing":[22],"client's":[24],"raw":[25],"data.":[26],"Nevertheless,":[27],"clients'":[29],"sensitive":[30],"information":[31],"can":[32,122],"still":[33],"be":[34],"exposed":[35],"to":[36,64,133],"adversaries":[37],"via":[38],"differential":[39,71],"attacks":[40],"on":[41,124,139],"messages":[42,79],"exchanged":[43,78],"between":[44],"parameter":[46],"server":[47],"and":[48,62],"clients.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53,116],"consider":[54],"widely":[56],"used":[57],"federating":[58],"averaging":[59],"(FedAvg)":[60],"algorithm":[61,93,120,126,145],"propose":[63],"enhance":[65],"data":[67],"privacy":[68,72],"by":[69,80],"(DP)":[73],"technique,":[74],"which":[75],"obfuscates":[76],"properly":[81],"adding":[82],"Gaussian":[83],"noise.":[84],"We":[85],"analytically":[86],"show":[87],"that":[88],"proposed":[90,144],"secure":[91],"FedAvg":[92],"maintains":[94],"an":[95],"O(l/T)":[96],"convergence":[97],"rate,":[98],"where":[99],"T":[100],"total":[103],"number":[104],"stochastic":[106],"gradient":[107],"descent":[108],"(SGD)":[109],"updates":[110],"for":[111],"local":[112],"parameters.":[114],"Moreover,":[115],"demonstrate":[117],"how":[118],"various":[119],"parameters":[121],"impact":[123],"communication":[127],"efficiency.":[128],"Experiment":[129],"results":[130,138],"are":[131],"presented":[132],"justify":[134],"obtained":[136],"analytical":[137],"performance":[141],"in":[146],"terms":[147],"testing":[149],"accuracy.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}