Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images. (arXiv:2208.10919v1 [cs.CR])

Federated learning (FL) is a decentralized method enabling hospitals to
collaboratively learn a model without sharing private patient data for
training. In FL, participant hospitals periodically exchange training results
rather than training samples with a central server. However, having access to
model parameters or gradients can expose private training data samples. To
address this challenge, we adopt secure multiparty computation (SMC) to
establish a privacy-preserving federated learning framework. In our proposed
method, the hospitals are divided into clusters. After local …

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