FedBot：利用联邦学习增强聊天机器人的隐私保护
FedBot: Enhancing Privacy in Chatbots with Federated Learning
AI总结：
本文提出 FedBot，一个结合 Deep Bidirectional Transformer 模型与联邦学习算法的隐私保护聊天机器人概念验证，旨在利用大规模客户支持数据在协作训练中保护用户数据隐私，同时提供满足法规要求的个性化高效服务并持续提升性能。
摘要：
聊天机器人主要由数据驱动，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会侵犯用户隐私。自聊天机器人诞生以来，这类问题就一直普遍存在。在现有文献中，已有许多处理隐私问题的方法，如差分隐私和安全多方计算，但其中大多数仍需要访问用户数据。在此背景下，Federated Learning (FL) 旨在通过将数据保留在原地的分布式学习方法来保护数据隐私。本文提出了 FedBot，这是一个利用大规模客户支持数据的概念验证（POC）隐私保护聊天机器人。该 POC 结合了 Deep Bidirectional Transformer 模型和联邦学习算法，以在协作模型训练期间保护客户数据隐私。该概念验证的结果展示了隐私保护聊天机器人在变革客户支持行业方面的潜力，它能够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。此外，该系统经过专门设计，能够利用从先前交互中学习的能力，随着时间的推移提升其性能和准确率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
