Privacy-preserving federated neural network learning for disease-associated cell classification.

ABSTRACT: Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, PriCell, for complex models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions' input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a publ

SUBMITTER: Sav S 

PROVIDER: S-EPMC9122966 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature