A federated collaborative recommendation model for privacy-preserving distributed recommender applications based on microservice framework

Abstract

The wide adoption of IoT technologies has accelerated the accumulation of big data. Recommender systems (RS) is one of the most effective methods to extract user interested items from the huge volume of big data. However, implementing a recommender system over the distributed error-prone IoT devices faces two challenges. On the one hand, the distributed IoT devices may randomly fail to deliver its local data due to hardware malfunction, which may cause unavailability of the recommender system. Moreover, collecting the raw data from the distributed IoT devices may cause data privacy leakage issue, since the privacy data of user-item interaction records may be abused by vicious parties. In view of these challenges, we propose a federated collaborative recommendation model based on microservice framework in this paper to implement privacy preserving distributed recommendation applications. Firstly, we utilize the federated learning framework to train the collaborative recommendation model, where the raw data on each distributed device is kept locally and only item related model parameters are exposed to train the federated recommendation model. Moreover, we adopt the microservice framework to encapsulate different functions of the federated recommender model. Each distributed device can participate in the federated training process via service registration and service discovery function of the microservice framework. Furthermore, we enhance the typical Neural Collaborative Filtering model with the proposed FedNeuMF model by fusing auxiliary user profiles and item attributes to improve the recommendation accuracy. Finally, we conduct a set of experiments on three real-world datasets to check the performance of our proposal.