Decentralized Federation Learning Based on Fed-DPDOBO
The traditional client-server architecture federation learning is an effective means of solving the problem of data silos,where the central server is under enormous bandwidth pressure and the decentralized peer-to-peer architecture federation learn-ing improves this situation to some extent.However,clients of federal learning also suffer from the risk of data privacy breaches and the gradient information of their cost function is difficult to obtain in some cases.To address these issues,this paper designs an Federated Differential Privacy Distributed One-point Bandit Online algorithm(Fed-DPDOBO)for peer-to-peer architecture federation learning under consistency constraints,which effectively addresses the problems of bandwidth limitation of the central node and unknown gradient information of the client.In addition,data privacy for each client is well protected due to the use of differential privacy technology.Finally,the effectiveness of this paper's algorithm is verified by conducting decentralized federa-tion learning experiments with the MINST dataset.
data silosfederated learningconsistency constraintspeer-to-peer architecturedifferential privacyone-point Bandit
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