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With the advent of big data era and the development of artificialintelligence and other technologies, data security and privacy protection havebecome more important. Recommendation systems have many applications in oursociety, but the model construction of recommendation systems is ofteninseparable from users' data. Especially for deep learning-based recommendationsystems, due to the complexity of the model and the characteristics of deeplearning itself, its training process not only requires long training time andabundant computational resources but also needs to use a large amount of userdata, which poses a considerable challenge in terms of data security andprivacy protection. How to train a distributed recommendation system whileensuring data security has become an urgent problem to be solved. In thispaper, we implement two schemes, Horizontal Federated Learning and SecureDistributed Training, based on Intel SGX(Software Guard Extensions), animplementation of a trusted execution environment, and TensorFlow framework, toachieve secure, distributed recommendation system-based learning schemes indifferent scenarios. We experiment on the classical Deep LearningRecommendation Model (DLRM), which is a neural network-based machine learningmodel designed for personalization and recommendation, and the results showthat our implementation introduces approximately no loss in model performance.The training speed is within acceptable limits.

Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX