FedDeepFM: A Factorization Machine-Based Neural Network for Recommendation in Federated Learning

Recommender systems are an effective way to address the information overload problem. Due to the rapid development of deep learning, recommender systems based on deep learning can efficiently process massive training samples and integrate a variety of additional information, which can alleviate the inherent data sparsity and cold start problems of recommender systems. However, deep learning recommender systems need to collect massive amounts of user data for training models, which poses challenges for data security and user privacy protection. By using users’ local, not centrally collected, data, federated learning can collaboratively train deep learning recommendation models and improve the accuracy of recommendations without compromising user privacy. In this paper, we propose FedDeepFM, a deep recommender system model based on federated learning. First, FedDeepFM follows the standard paradigm of federated learning by uploading each client’s model parameters instead of the original data for model updates, according to the data processing inequality, which ensures that the server cannot collect users’ private information directly but at the same time is able to use the private data held by each client to jointly train a global model with generalization capabilities. Second, to prevent the server from learning users’ private information through indirect inference methods, a pseudo-interaction filling method is used to expand the client data to mask the real user data. The experimental results show that FedDeepFM, the deep recommender system model based on federated learning proposed in this paper, can provide high-quality recommendations while enhancing users’ privacy.