Decentralized Federated Learning with Enhanced Privacy Preservation

Sheng-Po Tseng, Jan-Yue Lin, Wei-Chien Cheng, Lo-Yao Yeh, Chih-Ya Shen

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.