Boulez: A Chatbot-Based Federated Learning System for Distance Learning

In recent years, also due to the covid-19 pandemic, the possibilities for distance learning have increased considerably, through web-based learning platforms, available on the Internet without space and time limits. As a result, the offer of courses and the number of enrolled students has grown exponentially. In order to be able to guarantee students a better learning support service, one of the proposals regards the intelligent Chatbots. These well known interactive applications are based mainly on machine or deep learning and in this paper we present Boulez, a system allowing the orchestration of a community of individual chatbots, each one with its algorithm and its private training dataset. We apply a technique called Federated Learning, where several individual chatbots, collaborate. In particular, here the approach is 'centralized', meaning that a main system orchestrates the collaboration of the federated systems. By addressing the communication inefficiencies and privacy issues of conventional federated learning, Boulez offers a more efficient and effective approach to chatbot interaction, ultimately leading to improved user experience. The paper presents the Boulez system, its operation principle, methods used, and potential benefits, along with a use case of its application.