To address the issue of high communication cost under the federated learning framework in multi-tenant data cen-ters,this paper proposed an optimization algorithm based on ternary evolutionary model parameters.Firstly,it constructed a federated learning architecture tailored to multi-tenant data centers for data privacy protection.Secondly,in response to the ex-cessive communication overhead stemming from the implementation of the federated learning framework,which increasing interactions between tenants and the data center,it proposed an optimization algorithm that utilized ternary evolutionary model parameters.This algorithm aimed to reduce redundant communication in the exchange of model parameters between tenants and the data center by integrating the optimal local model with the evolutionary direction of ternary vectorized model parame-ters.Moreover,by analyzing privacy research based on federated learning,the algorithm effectively ensured the privacy of tenants participating in the training during the communication process.Finally,experimental results demonstrate that,while maintaining training accuracy,the proposed method can effectively reduce redundant communication costs by 30％compared to the federated averaging baseline algorithm.
关键词
多租户数据中心/联邦学习/通信开销优化/三元演化模型参数
Key words
multi-tenant data center/federated learning/optimization of communication cost/ternarizing evolution of model parameters
维普
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