  Nowadays, Deep Neural Networks are widely applied to various domains.
However, massive data collection required for deep neural network reveals the
potential privacy issues and also consumes large mounts of communication
bandwidth. To address these problems, we propose a privacy-preserving method
for the federated learning distributed system, operated on Intel Software Guard
Extensions, a set of instructions that increase the security of application
code and data. Meanwhile, the encrypted models make the transmission overhead
larger. Hence, we reduce the commutation cost by sparsification and it can
achieve reasonable accuracy with different model architectures.
