{"id":"https://openalex.org/W4407774281","doi":"https://doi.org/10.1145/3711129.3711310","title":"An Efficient Federated Learning Privacy Protection Algorithm based on Model Quantification","display_name":"An Efficient Federated Learning Privacy Protection Algorithm based on Model Quantification","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4407774281","doi":"https://doi.org/10.1145/3711129.3711310"},"language":"en","primary_location":{"id":"doi:10.1145/3711129.3711310","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3711129.3711310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering","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":null,"display_name":"Yufan Zhang","orcid":"https://orcid.org/0009-0009-7727-5989"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yufan Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"raw_orcid":"https://orcid.org/0009-0009-7727-5989","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045898677","display_name":"Tianya Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianya Cao","raw_affiliation_strings":["School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"raw_orcid":"https://orcid.org/0009-0006-0084-7457","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yujing Zhang","orcid":"https://orcid.org/0009-0003-7006-0295"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujing Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"raw_orcid":"https://orcid.org/0009-0003-7006-0295","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I68986083"],"apc_list":null,"apc_paid":null,"fwci":0.3311,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70061779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1064","last_page":"1069"},"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":0.9975000023841858,"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/T10927","display_name":"Access Control and Trust","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7859584093093872},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.6390266418457031},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5437465906143188},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4602915048599243},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4408138692378998},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.33079034090042114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2798313796520233},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1749858558177948}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7859584093093872},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.6390266418457031},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5437465906143188},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4602915048599243},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4408138692378998},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.33079034090042114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2798313796520233},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1749858558177948}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711129.3711310","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3711129.3711310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.41999998688697815,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2473418344","https://openalex.org/W2979417040","https://openalex.org/W2990138404","https://openalex.org/W3092144916","https://openalex.org/W3209696639","https://openalex.org/W4318619660","https://openalex.org/W6603827241"],"related_works":["https://openalex.org/W3022534164","https://openalex.org/W4403547452","https://openalex.org/W4396832952","https://openalex.org/W3046095319","https://openalex.org/W3197497514","https://openalex.org/W1591172238","https://openalex.org/W2111194702","https://openalex.org/W2972172135","https://openalex.org/W2116878667","https://openalex.org/W1787552957"],"abstract_inverted_index":{"Federated":[0],"learning,":[1],"as":[2],"a":[3,140],"distributed":[4],"machine":[5],"learning":[6,29,98],"method,":[7],"enables":[8],"data":[9,64,138],"sets":[10],"from":[11],"different":[12],"clients":[13],"to":[14,30,34,41,55,65,78,118,121,134,139,143],"train":[15],"high-quality":[16],"models":[17],"without":[18],"leaving":[19],"the":[20,25,35,44,48,57,60,66,80,112,115,119,123,126,136,149,153],"local":[21],"area.":[22],"However,":[23],"in":[24,53],"process":[26],"of":[27,50,82,125],"federation":[28],"upload":[31],"model":[32,105,116,129,156],"parameters":[33,117,130],"server,":[36],"privacy":[37,83,99,124],"leakage":[38,84],"is":[39],"easy":[40],"occur.":[42],"At":[43],"same":[45],"time,":[46],"with":[47],"increase":[49],"training":[51],"rounds,":[52],"order":[54],"achieve":[56],"expected":[58],"effect,":[59],"client":[61,90,113,164],"frequently":[62],"uploads":[63,114],"server":[67,120],"during":[68,89],"each":[69],"training,":[70],"which":[71],"will":[72],"cause":[73],"certain":[74],"memory":[75,87,146,159],"consumption.":[76,147],"Therefore,":[77],"solve":[79],"problem":[81],"and":[85,158],"excessive":[86],"consumption":[88,160],"upload,":[91],"this":[92],"paper":[93],"proposes":[94],"an":[95],"efficient":[96],"federated":[97],"protection":[100],"algorithm":[101,108],"(FedEMQ)":[102],"based":[103],"on":[104],"quantification.":[106],"The":[107,128],"adds":[109],"noise":[110],"before":[111],"protect":[122],"client.":[127],"are":[131],"then":[132],"quantized":[133],"convert":[135],"floating-point":[137],"low-precision":[141],"type":[142],"reduce":[144],"its":[145],"Finally,":[148],"experimental":[150],"show":[151],"that":[152],"system":[154],"balances":[155],"performance":[157],"well":[161],"while":[162],"protecting":[163],"privacy.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}