基于用戶選擇的魯棒與隱私保護聯(lián)邦學習方案

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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

隨著機器學習技術(shù)的快速發(fā)展，人工智能在各個領域的應用得到廣泛發(fā)展。（剩余24804字）