A Personalized Federal Power Load Forecasting Scheme Based on Differential Privacy
In order to achieve a power load forecasting scheme with both model personalization and privacy-preserving personalization,this paper proposes a personalized federal power load forecasting scheme based on differential privacy. The scheme performs cluster-based training based on the missing cases and temporal features of data to obtain a local personalized model applicable to local data. On this basis,a personalized differential privacy protection scheme is proposed,which adjusts the allocation of the privacy budget according to the distance from the client to the current cluster center to ensure the data security and achieve the personalization of privacy protection at the client level. Experiments show that the algorithm can be trained to obtain a personalization model with better utility while ensuring data security.
power load forecastingpersonalized federal learningdifferential privacyprivacy protectionprivacy budgetclustering
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