基于用户选择的鲁棒与隐私保护联邦学习方案

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Robust and privacy-preserving federated learning scheme based on user selection

Wang Xiaoming1.²，Huang Binrui2+ （1.Colegeflelcge；ofo gy ，Jinan University，Guangzhou 510632，China）

Abstract：Tocounterthevulnerabilitiesofmodelparameters toinferenceandByzantineattcksduringfederatedlearning，this paperproposed arobustand privacy-preserving federated learning scheme basedonuserselection，enhancing thesecurityand reliabilityofmodel training.Itfirstlydesignedauserselectionalgorithmbasedontheconceptof groups constructedonfog servers.Thepurposeofthisalgorithmwastoselectuserswithhighcreditscores tocontribute tothetrainingof theglobal model.Next，itconstructedamethodforfiltering local modelparametersandupdatinguserscoresusingthetestsetfromthe cloudserver，efectivelymitigatingtheinterference frommalicious usersinthemodel training processandprogresivelyexcludingthemfromtraining，therebyenhancingtherobustnessoftheglobalmodel.Finally，itdesignedalightweightencryption algorithmbasedoncloud-fogcollaboration，whichnotonlyefectivelyprotectedtheprivacyofuserlocalmodelparametersbut alsoensuredtheirsecurityduringtheagregationprocess，whilemaintaining highcomputationalandcommunicationeffciency. Buildinguponthecomputationalchallngeof theDifie-Hellman（CDH）problem，itdemonstratedthesecurityof thisscheme， whichresistedvarious atacks，ensuring theglobal model’srobustness whilesafeguarding userdata privacy.Bycomparing with existing schemes andthrough performance analysisand experimental results，the proposal exceled in eficiency.When facing maliciousattackers，the accuracy rates of directly aggregated global models dropped to about 65% ，whereas this scheme maintainedanaccuracyrateclosetothatofasenariowithoutatacks，ffctivelymtigatingtheimpactofatacks.Tus，thissolution offersapractical and efective strategy for federated learning systems todeal with inference and Byzantineattcks.

Keywords：federated learning；robustness；privacy preservation；selecting user

0 引言

随着机器学习技术的快速发展，人工智能在各个领域的应用得到广泛发展。（剩余24804字）