title: Secure Quantized Training for Deep Learning
abstract: We have implemented training of neural networks in secure multi-party
  computation (MPC) using quantization commonly used in said setting. To
  the best of our knowledge, we are the first to present training of
  MNIST purely implemented in MPC that comes within one percent of
  accuracy of training using plaintext computation.  We found that
  training with MPC is possible, but it takes more epochs and achieves a
  lower accuracy than the usual CPU/GPU computation.  More concretely,
  we have trained a network with two convolution and two dense layers to
  98.5% accuracy in 150 epochs. This took a day in our MPC
  implementation.
  
authors: Marcel Keller Ke Sun
hIndex: Marcel Keller 19