  With the advent of big data era and the development of artificial
intelligence and other technologies, data security and privacy protection have
become more important. Recommendation systems have many applications in our
society, but the model construction of recommendation systems is often
inseparable from users' data. Especially for deep learning-based recommendation
systems, due to the complexity of the model and the characteristics of deep
learning itself, its training process not only requires long training time and
abundant computational resources but also needs to use a large amount of user
data, which poses a considerable challenge in terms of data security and
privacy protection. How to train a distributed recommendation system while
ensuring data security has become an urgent problem to be solved. In this
paper, we implement two schemes, Horizontal Federated Learning and Secure
Distributed Training, based on Intel SGX(Software Guard Extensions), an
implementation of a trusted execution environment, and TensorFlow framework, to
achieve secure, distributed recommendation system-based learning schemes in
different scenarios. We experiment on the classical Deep Learning
Recommendation Model (DLRM), which is a neural network-based machine learning
model designed for personalization and recommendation, and the results show
that our implementation introduces approximately no loss in model performance.
The training speed is within acceptable limits.
