A Novel Privacy-Preserving Computing System Based on VAE Federated Meta-Learning

Abstract

In recent years, the release of data value under privacy-preserving has always been the research focus. As an essential data protection technology, encrypted computing provides a good solution. Based on this, a federated learning model is first constructed to meet the needs of participants using local data for joint modeling. Secondly, the meta-learning method accelerates the federated learning model’s training effect and improves the accuracy of model detection. Finally, the variational auto-encoder is used to optimize the federated learning model, which speeds up the training of the federated learning model and improves the system’s overall security. Experimental results show that the proposed system can provide secure computing conditions for each data provider and better performance.

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