We have implemented training of neural networks in secure multi-party computation (MPC) using quantization commonly used in the said setting. To the best of our knowledge, we are the first to present an MNIST classifier purely trained in MPC that comes within 0.2 percent of the accuracy of the same convolutional neural network trained via plaintext computation. More concretely, we have trained a network with two convolution and two dense layers to 99.2% accuracy in 25 epochs. This took 3.5 hours in our MPC implementation (under one hour for 99% accuracy).
翻译:我们用上述环境常用的量化方法对神经网络进行了安全多党计算(MPC)的培训。 据我们所知,我们是第一个提出一个纯受过MPC培训的MNIST分类员,该分类员的精度不超过通过平文本计算培训的同一卷发神经网络精度的0.2%。更具体地说,我们在25个时代对一个具有两层混凝土和两层稠密的网络进行了培训,精确度达到99.2%。这需要我们执行MPC的3.5小时时间(精确度不到1小时99% ) 。