Federated Learning in Chat-Bots: Collaborative Threat Detection Across Decentralized Networks

By: Pinaki Sahu, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, 0000pinaki1234.kv@gmail.com

Abstract

Federated Learning (FL) in chatbots for collaborative threat detection across decentralized networks is explored in this article. FL protects user privacy by allowing chatbots to train models on local data. Reduced transmission of information overhead, improved privacy, and flexibility to local circumstances are benefits. FL’s ability to withstand centralized attacks is essential for protecting user communications. Notwithstanding obstacles such as model convergence, secure aggregation, and bias management provide answers. The use of FL in chatbots strengthens cybersecurity and sets the standard for ethical, privacy-focused AI development in decentralized environments.

Introduction

Strong threat detection methods are becoming increasingly important as the digital ecosystem changes. In the world of chatbots, which are extensively used for a variety of purposes from virtual assistants to customer service, protecting the confidentiality and security of user communications is critical. In order to solve these problems, Federated Learning (FL) emerges as an effective paradigm that enables chatbots to cooperatively identify risks within decentralized networks. We explore the role that federated learning serves in improving chat-bot threat detection in decentralized environments .

Understanding Federated Learning

Federated learning is a machine learning technique that allows models to be trained without data exchange among decentralised devices or servers that store local data samples. Federated learning promotes privacy and lowers the risks associated with data breaches by enabling the model to be trained on local devices rather than centralizing data in one place. This decentralised structure is in line with chatbot architecture, which places a high value on data security and privacy[1].

Combined Threat Identification:

As essential components of online platforms and services, chatbots are vulnerable to a variety of threats, including phishing attempts, the spread of harmful information, and other assaults. Conventional threat detection techniques frequently depend on centralized models that might compromise user privacy and become vulnerable to data breaches. By enabling chatbots to cooperatively learn from and update threat detection models without exchanging sensitive user data, federated learning reduces these risks[2].

Fig.1. Federated Learning in chatbot[2]

Benefits of Federated Learning in Chat-Bots:

Fig.2. Benefits after integrating federated learning in chat-bots[3]

• Privacy Protection: Chatbots can gain knowledge from user interactions because of Federated Learning, which keeps private information from being sent to a central server. By maintaining user privacy through a decentralized manner, customer trust is increased[3].
• Flexibility in Various Local Contexts: Chatbots frequently serve a wide range of users with various language quirks and contextual distinctions. By training on data unique to each decentralized node, federated learning enables models to adjust to different local circumstances, increasing the overall accuracy of threat detection.
• Reduced Data Transfer Overhead: Federated learning reduces the amount of data transmitted between nodes by simply communicating model updates as opposed to raw data. In addition to increasing productivity, this reduces the possibility of data interception risk during transit[3].
• Resilience to Centralised Attacks: Attacks aimed at centralised servers might affect traditional threat detection systems. By distributing the learning process, federated learning increases its resistance to these kinds of assaults and maintains threat detection capabilities.

Implementation Challenges and Their Solutions:

Federated learning presents several interesting advantages, but integrating it with chatbots presents unique difficulties. Among the difficulties include resolving possible biases in local datasets, managing communication costs, and guaranteeing model convergence. Among the solutions are secure aggregation approaches, communication protocol optimization, and dataset bias mitigation strategies[4].

Conclusion

Federated Learning offers a novel method for improving chatbot threat detection in decentralised networks. Through the prioritisation of privacy, flexibility, and resilience, this collaborative learning paradigm provides a strong defence against the constantly changing cyber threat scenario. Federated learning is a shining example of how to efficiently and privately secure user interactions as the usage of chatbots increases. Federated learning in chatbots strengthens cybersecurity and establishes a standard for ethical and user-centred AI development.

References

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Cite As

Sahu P. (2023) Federated Learning in Chat-Bots: Collaborative Threat Detection Across Decentralized Networks, Insights2Techinfo, pp.1

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