{"id":"https://openalex.org/W4410772408","doi":"https://doi.org/10.1145/3727353.3727468","title":"An Efficient Privacy-Preserving Federated Learning Scheme for Collusion Resistance and Dropout Prevention","display_name":"An Efficient Privacy-Preserving Federated Learning Scheme for Collusion Resistance and Dropout Prevention","publication_year":2025,"publication_date":"2025-01-10","ids":{"openalex":"https://openalex.org/W4410772408","doi":"https://doi.org/10.1145/3727353.3727468"},"language":"en","primary_location":{"id":"doi:10.1145/3727353.3727468","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727468","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727468","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727468","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088214867","display_name":"Xinyue Zhang","orcid":"https://orcid.org/0009-0006-2954-4804"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyue Zhang","raw_affiliation_strings":["College of Cryptography Engineering, Engineering University of PAP, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0006-2954-4804","affiliations":[{"raw_affiliation_string":"College of Cryptography Engineering, Engineering University of PAP, Xi'an, Shaanxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104108843","display_name":"Ke Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Ke","raw_affiliation_strings":["College of Cryptography Engineering, Engineering University of PAP, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0001-8728-4354","affiliations":[{"raw_affiliation_string":"College of Cryptography Engineering, Engineering University of PAP, Xi'an, Shaanxi, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101231391","display_name":"Zijun Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zijun Guo","raw_affiliation_strings":["College of Cryptography Engineering, Engineering University of PAP, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0002-1418-9556","affiliations":[{"raw_affiliation_string":"College of Cryptography Engineering, Engineering University of PAP, Xi'an, Shaanxi, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07257656,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"716","last_page":"721"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9966999888420105,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/collusion","display_name":"Collusion","score":0.9225040674209595},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.7155231833457947},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6469844579696655},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6354992389678955},{"id":"https://openalex.org/keywords/resistance","display_name":"Resistance (ecology)","score":0.5792702436447144},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.5102782249450684},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.5012085437774658},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.44097819924354553},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.2420843541622162},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2018640637397766},{"id":"https://openalex.org/keywords/industrial-organization","display_name":"Industrial organization","score":0.09822586178779602}],"concepts":[{"id":"https://openalex.org/C2781198186","wikidata":"https://www.wikidata.org/wiki/Q701521","display_name":"Collusion","level":2,"score":0.9225040674209595},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.7155231833457947},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6469844579696655},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6354992389678955},{"id":"https://openalex.org/C57473165","wikidata":"https://www.wikidata.org/wiki/Q7315604","display_name":"Resistance (ecology)","level":2,"score":0.5792702436447144},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.5102782249450684},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5012085437774658},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.44097819924354553},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2420843541622162},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2018640637397766},{"id":"https://openalex.org/C40700","wikidata":"https://www.wikidata.org/wiki/Q1411783","display_name":"Industrial organization","level":1,"score":0.09822586178779602},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3727353.3727468","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727468","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727468","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3727353.3727468","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727468","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727468","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410772408.pdf","grobid_xml":"https://content.openalex.org/works/W4410772408.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2090653623","https://openalex.org/W2767079719","https://openalex.org/W2912213068","https://openalex.org/W2960799464","https://openalex.org/W3012549930","https://openalex.org/W3036493655","https://openalex.org/W3153347425","https://openalex.org/W4213044365","https://openalex.org/W4244373353","https://openalex.org/W4250955649","https://openalex.org/W4377007936","https://openalex.org/W6832722821"],"related_works":["https://openalex.org/W2009938035","https://openalex.org/W3143020802","https://openalex.org/W1491837399","https://openalex.org/W3122232980","https://openalex.org/W2272761499","https://openalex.org/W2363272970","https://openalex.org/W2120306875","https://openalex.org/W1699588379","https://openalex.org/W3143060468","https://openalex.org/W3215381757"],"abstract_inverted_index":{"In":[0,47],"the":[1,35,63,69,73,77,80,99,104,111,130,138,142,145,154,166],"field":[2],"of":[3,37,65,79,106,113,144],"distributed":[4],"machine":[5],"learning,":[6],"federated":[7,94],"learning":[8,95],"(FL)":[9],"is":[10,84],"used":[11],"as":[12],"a":[13,25,119],"data":[14,115],"privacy-preserving":[15],"technique":[16],"that":[17,52,129,151],"allows":[18],"multiple":[19],"clients":[20],"to":[21,153],"collaborate":[22],"in":[23,44,62,141],"training":[24],"shared":[26],"model":[27],"without":[28],"disclosing":[29],"local":[30],"data.":[31],"However,":[32],"FL":[33],"faces":[34],"challenges":[36],"inefficient":[38],"communication":[39,121],"and":[40,87,102,108,117,161],"high":[41],"computational":[42],"cost":[43],"practical":[45],"applications.":[46],"this":[48,89,157],"paper,":[49],"we":[50],"find":[51],"EPFL":[53,81],"schemes":[54],"have":[55],"certain":[56],"security":[57],"flaws,":[58],"which":[59,83,97],"will":[60],"result":[61],"leakage":[64],"user":[66],"privacy":[67],"once":[68],"server":[70,131],"colludes":[71],"with":[72],"clients.":[74],"To":[75],"address":[76],"shortcomings":[78],"scheme":[82,158],"not":[85],"anti-collusion":[86,107,160],"anti-dropout,":[88],"paper":[90],"proposes":[91],"an":[92],"improved":[93],"algorithm,":[96],"adopts":[98],"Flamingo":[100],"protocol,":[101],"has":[103,118,159],"properties":[105],"anti-dropout":[109,162],"under":[110],"premise":[112],"ensuring":[114],"privacy,":[116],"low":[120],"volume.":[122],"A":[123],"lightweight":[124],"exit":[125],"resilience":[126],"protocol":[127],"ensures":[128],"can":[132],"obtain":[133],"meaningful":[134],"results":[135,149],"even":[136],"if":[137],"client":[139],"exits":[140],"middle":[143],"process.":[146],"The":[147],"analysis":[148],"show":[150],"compared":[152],"original":[155],"scheme,":[156],"features":[163],"while":[164],"maintaining":[165],"model's":[167],"accuracy.":[168]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
