Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Abstract

We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.

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