Cloud deep learning platforms provide cost-effective deep neural network
(DNN) training for customers who lack computation resources. However, cloud
systems are often untrustworthy and vulnerable to attackers, leading to growing
concerns about model privacy. Recently, researchers have sought to protect data
privacy in deep learning by leveraging CPU trusted execution environments
(TEEs), which minimize the use of cryptography, but existing works failed to
simultaneously utilize the computational resources of GPUs to assist in
training and prevent model leakage. This paper presents Tempo, the first
cloud-based deep learning system that cooperates with TEE and distributed GPUs
for efficient DNN training with model confidentiality preserved. To tackle the
challenge of preserving privacy while offloading linear algebraic operations
from TEE to GPUs for efficient batch computation, we introduce a customized
permutation-based obfuscation algorithm to blind both inputs and model
parameters. An optimization mechanism that reduces encryption operations is
proposed for faster weight updates during backpropagation to speed up training.
We implement Tempo and evaluate it with both training and inference for two
prevalent DNNs. Empirical results indicate that Tempo outperforms baselines and
offers sufficient privacy protection.

Tempo 是首个与 TEE 和分布式 GPU 合作的基于云的深度学习系统，通过引入以排列为基础的隐匿算法以盲化输入和模型参数，并提出了一种优化机制来减少加密操作，从而在保护模型隐私的同时加速了深度神经网络的训练，实验证明 Tempo 优于基准方法并提供足够的隐私保护。

云端神经网络训练中的保密性保护

Tempo: Confidentiality Preservation in Cloud-Based Neural Network  Training

Multi-tenant machine learning services have become emerging data-intensive
workloads in data centers with heavy usage of GPU resources. Due to the large
scale, many tuning parameters and heavy resource usage, it is usually
impractical to evaluate and benchmark those machine learning services on real
clusters. In this demonstration, we present AnalySIM, a cluster simulator that
allows efficient design explorations for multi-tenant machine learning
services. Specifically, by trace-driven cluster workload simulation, AnalySIM
can easily test and analyze various scheduling policies in a number of
performance metrics such as GPU resource utilization. AnalySIM simulates the
cluster computational resource based on both physical topology and logical
partition. The tool has been used in SenseTime to understand the impact of
different scheduling policies with the trace from a real production cluster of
over 1000 GPUs. We find that preemption and migration are able to significantly
reduce average job completion time and mitigate the resource fragmentation
problem.

AnalySIM 是一个集群模拟器，可通过跟踪驱动的集群工作负载模拟，以各种性能指标（如 GPU 资源利用率）测试和分析各种调度策略，具有物理拓扑和逻辑分区两种基于集群计算资源的模拟方式。