Privacy-preserving cross-network service recommendation via federated learning of unified user representations

With the emergence of cloud computing, the Internet of Things, and other large-scale environments, recommender systems have been faced with several issues, mainly (i) the distribution of user-item data across multiple information networks, (ii) privacy restrictions and the partial profiling of users and items caused by this distribution, (iii) the heterogeneity of user- item knowledge in different information networks. Furthermore, most approaches perform recommendations based on a single source of information, and do not handle the partial representation of users' and items' information in a federated way. Such isolated and non- collaborative behavior, in multi-source and cross-network information settings, often results in inaccurate and low-quality recommendations. To address these issues, we exploit the strengths of network representation learning and federated learning to propose a service recommendation approach in smart service networks. While NRL is employed to learn rich representations of entities (e.g., users, services, IoT objects), federated learning helps collaboratively infer a unified profile of users and items, based on the concept of anchor user, which are bridge entities connecting multiple information networks. These unified profiles are, finally, fed into a federated recommendation algorithm to select the top-rated services. Using a scenario from the smart healthcare context, the proposed approach was developed and validated on a multiplex information network built from real-world electronic medical records (157 diseases, 491 symptoms, 273 174 patients, treatments and anchors data). Experimental results under varied federated settings demonstrated the utility of cross-client knowledge (i.e. anchor links) and the collaborative reconstruction of composite embeddings (i.e. user representations) for improving recommendation accuracy. In terms of RMSE@K and MAE@K, our approach achieved an improvement of 54.41% compared to traditional single-network recommendation, as long as the federation and communication scale increased. Moreover, the gap with four federated approaches has reached 19.83 %, highlighting our approach's ability to map local embeddings (i.e. user's partial representations) into a complete view.