In this paper, we address the problem of privacy-preserving training and
evaluation of neural networks in an $N$-party, federated learning setting. We
propose a novel system, POSEIDON, the first of its kind in the regime of
privacy-preserving neural network training. It employs multiparty lattice-based
cryptography to preserve the confidentiality of the training data, the model,
and the evaluation data, under a passive-adversary model and collusions between
up to $N-1$ parties. To efficiently execute the secure backpropagation
algorithm for training neural networks, we provide a generic packing approach
that enables Single Instruction, Multiple Data (SIMD) operations on encrypted
data. We also introduce arbitrary linear transformations within the
cryptographic bootstrapping operation, optimizing the costly cryptographic
computations over the parties, and we define a constrained optimization problem
for choosing the cryptographic parameters. Our experimental results show that
POSEIDON achieves accuracy similar to centralized or decentralized non-private
approaches and that its computation and communication overhead scales linearly
with the number of parties. POSEIDON trains a 3-layer neural network on the
MNIST dataset with 784 features and 60K samples distributed among 10 parties in
less than 2 hours.

本文介绍了 POSEIDON 系统，使用多方格基密码学在 N 方联邦学习情境下保留训练和评估数据的保密性，以及提出了通用的打包方法来在加密数据上执行单指令多数据操作的安全反向传播算法，结果表明 POSEIDON 的准确率与中心化或分散式非私人方法相当，同时其计算和通讯开销随参与方数量线性缩放。