Federated Learning on Recommender Systems

Recommender Systems have greatly affected how we consume services, products, and content in recent years. They have various applications in everyday life, such as clothing, restaurants, or song recommendations, rating predictions and businesses to help them understand user choices well. While these systems enhance the user experience, there are concerns about data privacy. Such systems collect private information about users based on their online behavior, cookies, and social interactions, such as user clicks, time, and other data points to improve recommendations. This centralized approach to collecting and storing information is prone to privacy risks and data breaches. The primary goal of this work is to explore the potential of federated learning in addressing these privacy and security issues in recommender systems. We evaluate four algorithms in a federated and non-federated setting across seven diverse datasets to benchmark the performance of federated learning and provide insights into the efficacy of the approach in preserving privacy while maintaining recommender system performance. In this approach, the models are trained on edge devices using the data on user machines. This technique shares each user’s updated parameters using optimal aggregation functions instead of actual data to a shared server. This decentralized way ensures that the data remains local and protects data privacy. We share our code and framework details to enable replication and further expansion of this benchmark work to more datasets and algorithms from the scientific community.