  Chatbots are mainly data-driven and usually based on utterances that might be
sensitive. However, training deep learning models on shared data can violate
user privacy. Such issues have commonly existed in chatbots since their
inception. In the literature, there have been many approaches to deal with
privacy, such as differential privacy and secure multi-party computation, but
most of them need to have access to users' data. In this context, Federated
Learning (FL) aims to protect data privacy through distributed learning methods
that keep the data in its location. This paper presents Fedbot, a
proof-of-concept (POC) privacy-preserving chatbot that leverages large-scale
customer support data. The POC combines Deep Bidirectional Transformer models
and federated learning algorithms to protect customer data privacy during
collaborative model training. The results of the proof-of-concept showcase the
potential for privacy-preserving chatbots to transform the customer support
industry by delivering personalized and efficient customer service that meets
data privacy regulations and legal requirements. Furthermore, the system is
specifically designed to improve its performance and accuracy over time by
leveraging its ability to learn from previous interactions.
