{"id":"https://openalex.org/W4403296901","doi":"https://doi.org/10.1109/tc.2024.3477971","title":"Balancing Privacy and Accuracy Using Significant Gradient Protection in Federated Learning","display_name":"Balancing Privacy and Accuracy Using Significant Gradient Protection in Federated Learning","publication_year":2024,"publication_date":"2024-10-10","ids":{"openalex":"https://openalex.org/W4403296901","doi":"https://doi.org/10.1109/tc.2024.3477971"},"language":"en","primary_location":{"id":"doi:10.1109/tc.2024.3477971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tc.2024.3477971","pdf_url":null,"source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"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 Computers","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/A5111027049","display_name":"Benteng Zhang","orcid":"https://orcid.org/0009-0006-6946-5254"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Benteng Zhang","raw_affiliation_strings":["College of Computer Science and Software Engineering, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113881053","display_name":"Yingchi Mao","orcid":"https://orcid.org/0000-0002-9884-8100"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingchi Mao","raw_affiliation_strings":["College of Computer Science and Software Engineering, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068227459","display_name":"Xiaoming He","orcid":"https://orcid.org/0000-0003-4196-3041"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming He","raw_affiliation_strings":["College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047931252","display_name":"Huawei Huang","orcid":"https://orcid.org/0000-0002-7035-6446"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawei Huang","raw_affiliation_strings":["School of Software Engineering, Sun Yat-Sen University, Zhuhai, China","School of Software Engineering, Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Sun Yat-Sen University, Zhuhai, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Software Engineering, Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070206873","display_name":"Jie Wu","orcid":"https://orcid.org/0000-0003-0914-7546"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Wu","raw_affiliation_strings":["Center for Networked Computing, Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Center for Networked Computing, Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5111027049"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":3.6261,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.93861615,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"74","issue":"1","first_page":"278","last_page":"292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.998199999332428,"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":0.998199999332428,"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":0.926800012588501,"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":0.7461727857589722},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.6094120144844055},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5526134967803955},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.43215280771255493},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3840653598308563},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.3393183946609497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3317943215370178}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7461727857589722},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.6094120144844055},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5526134967803955},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.43215280771255493},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3840653598308563},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.3393183946609497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3317943215370178}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tc.2024.3477971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tc.2024.3477971","pdf_url":null,"source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"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 Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1979493600","https://openalex.org/W2194775991","https://openalex.org/W2473418344","https://openalex.org/W2794567274","https://openalex.org/W2912213068","https://openalex.org/W2930926105","https://openalex.org/W2946363484","https://openalex.org/W2963378725","https://openalex.org/W3048641832","https://openalex.org/W3091890527","https://openalex.org/W3116386889","https://openalex.org/W3121442875","https://openalex.org/W3135606723","https://openalex.org/W3138815606","https://openalex.org/W3157481694","https://openalex.org/W3162868875","https://openalex.org/W3172811949","https://openalex.org/W4205228770","https://openalex.org/W4225796852","https://openalex.org/W4280575929","https://openalex.org/W4283204848","https://openalex.org/W4312867640","https://openalex.org/W4353033995","https://openalex.org/W4360976870","https://openalex.org/W4377101727","https://openalex.org/W4384787634","https://openalex.org/W4387068389","https://openalex.org/W4389722404","https://openalex.org/W6728757088","https://openalex.org/W6746720608","https://openalex.org/W6751754709","https://openalex.org/W6757641797","https://openalex.org/W6767676916","https://openalex.org/W6773976177","https://openalex.org/W6787972765","https://openalex.org/W6798772894","https://openalex.org/W6841619692","https://openalex.org/W6849918048"],"related_works":["https://openalex.org/W2584827882","https://openalex.org/W2538581760","https://openalex.org/W3195097297","https://openalex.org/W3038106605","https://openalex.org/W2513267613","https://openalex.org/W3049084372","https://openalex.org/W2528109871","https://openalex.org/W2940702331","https://openalex.org/W4225340788","https://openalex.org/W4318485713"],"abstract_inverted_index":{"Previous":[0],"state-of-the-art":[1],"studies":[2],"have":[3],"demonstrated":[4],"that":[5,150,219],"adversaries":[6],"can":[7,75,124],"access":[8],"sensitive":[9],"user":[10],"data":[11],"by":[12,128,140],"membership":[13],"inference":[14],"attacks":[15],"(MIAs)":[16],"in":[17,40,51,81,102],"Federated":[18],"Learning":[19],"(FL).":[20],"Introducing":[21],"differential":[22,113],"privacy":[23,36,60,94,114,154,177,228],"(DP)":[24],"into":[25,70],"the":[26,35,82,85,92,134,137,186,212,227],"FL":[27],"framework":[28],"is":[29,63,199,221],"an":[30,109],"effective":[31],"way":[32],"to":[33,77,90,157],"enhance":[34],"of":[37,66,84,136,191,231],"FL.":[38],"Nevertheless,":[39],"differentially":[41],"private":[42],"federated":[43,115],"learning":[44],"(DP-FL),":[45],"local":[46],"gradients":[47,131],"become":[48],"excessively":[49],"sparse":[50],"certain":[52],"training":[53,57],"rounds.":[54],"Especially":[55],"when":[56],"with":[58],"low":[59],"budgets,":[61],"there":[62],"a":[64,78,100,222],"risk":[65],"introducing":[67],"excessive":[68,126],"noise":[69,171],"clients\u2019":[71],"gradients.":[72,146],"This":[73],"issue":[74],"lead":[76],"significant":[79,119,130],"degradation":[80],"accuracy":[83,98,190,230],"global":[86,96,138,188],"model.":[87],"Thus,":[88],"how":[89],"balance":[91],"user's":[93],"and":[95,132,159,176,180,196,207,229],"model":[97,139,183],"becomes":[99],"challenge":[101],"DP-FL.":[103,232],"To":[104],"this":[105],"end,":[106],"we":[107],"propose":[108],"approach,":[110],"known":[111],"as":[112],"aggregation,":[116],"based":[117,163,172],"on":[118,164,173],"gradient":[120,165],"protection":[121,155],"(DP-FedASGP).":[122],"DP-FedASGP":[123,151,192,220],"mitigate":[125],"noises":[127],"protecting":[129],"accelerate":[133],"convergence":[135],"calculating":[141],"dynamic":[142],"aggregation":[143],"weights":[144],"for":[145,225],"Experimental":[147],"results":[148],"show":[149],"achieves":[152],"comparable":[153],"effects":[156],"DP-FedAvg":[158],"cpSGD":[160],"(communication-private":[161],"SGD":[162],"quantization)":[166],"but":[167],"outperforms":[168],"DP-FedSNLC":[169],"(sparse":[170],"clipping":[174],"losses":[175],"budget":[178],"costs)":[179],"FedSMP":[181],"(sparsified":[182],"perturbation).":[184],"Furthermore,":[185],"average":[187],"test":[189],"across":[193],"four":[194],"datasets":[195],"three":[197],"models":[198],"about":[200],"<inline-formula><tex-math":[201,203,205,208],"notation=\"LaTeX\">$2.62$</tex-math></inline-formula>%,":[202],"notation=\"LaTeX\">$4.71$</tex-math></inline-formula>%,":[204],"notation=\"LaTeX\">$0.45$</tex-math></inline-formula>%,":[206],"notation=\"LaTeX\">$0.19$</tex-math></inline-formula>%":[209],"higher":[210],"than":[211],"above":[213],"methods,":[214],"respectively.":[215],"These":[216],"improvements":[217],"indicate":[218],"promising":[223],"approach":[224],"balancing":[226]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
