A Recommender System that safeguards the user privacy through Federated Learning
Ibai Guillén-Pacho, Rubén Sánchez, Diego Casado–Mansilla · 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) · 2021
Nowadays, there are more and more legal and ethical limitations when it comes to managing personal data. This is a serious problem for recommender systems (RS) or similar aplications since personal data (e.g. socioeconomic, demographics, behavioural, etc.) are the most useful for generating tailored correlations and predictions. To cope with this issue, this work proposes a recommender system that safeguards the user privacy by using a Federated Learning approach (FL). To this end, this article takes as the baseline an already existing centralized RS that uses all the data from users in a clear manner. This baseline RS is based on Factor Machines and it aims to employ persuasion strategies adapted to the user to increase energy awareness and change their consumption habits in the work environment. In order to test the performance of the FL-based distributed RS, the real dataset used (N=678) have been separated into four subsets mimicking a segmentation by country of origin (Austria, UK, Spain and Greece). Each country can create an artificial intelligence model suitable for its users that will be sent to a central server where the aggregation of models will take place and the improved global model will be returned back to each country. The simulation of this FL strategy is performed with four Raspberry Pi's reflecting each country and an NVIDIA Jetson Nano is used as the aggregation server. The generated model not only increases the privacy of the users as no raw data travels to the central server but also improves the reliability of the recommendations.