Decentralized Federated Learning with Enhanced Privacy Preservation

Abstract

We present a decentralized federated learning (FL) framework based on blockchain. In traditional federated learning, it is necessary that a third-party centralized server aggregates all the gradients which participant in the upload, but such a trusted third-party may not always exist. We address this issue with the decentralized blockchain and encrypt the neural network model parameters and gradients.