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.

翻译：聊天机器人主要依赖数据驱动，且通常基于可能包含敏感信息的用户话语。然而，在共享数据上训练深度学习模型可能侵犯用户隐私。自聊天机器人诞生以来，此类问题便普遍存在。文献中已有多种隐私保护方法，例如差分隐私和安全多方计算，但大多数方法仍需访问用户数据。在此背景下，联邦学习旨在通过将数据保留在本地端的分布式学习方法保护数据隐私。本文提出Fedbot，一个利用大规模客户支持数据的概念验证型隐私保护聊天机器人。该概念验证结合了深度双向Transformer模型与联邦学习算法，以在协作模型训练期间保护客户数据隐私。概念验证的结果展示了隐私保护聊天机器人通过提供满足数据隐私法规和法律要求的个性化高效客户服务，对客户支持行业进行变革的潜力。此外，该系统专门设计用于通过利用从先前交互中学习的能力，随时间的推移持续提升其性能与准确性。