DT-FU: Digital Twin-Driven Federated Unlearning for Resilient Vehicular Networks in the 6G Era

In the evolving landscape of federated learning management systems (FLMS), ensuring robust security against adversarial attacks is paramount. However, traditional methods that filter harmful updates before aggregation often fail in already compromised networks. This research introduces a pioneering approach that integrates digital twin (DT) technology with an FLMS, bolstering the network's resilience and safeguarding communication among distributed networks in the 6G era. At the heart of our methodology is the novel federated unlearning techniques, designed to mitigate the influence of malicious or poisonous clients within an already compromised network. By leveraging the DTs of clients, the system can effectively tackle such threats while ensuring the integrity of the network. Federated learning enables collaborative model training across decentralized clients, while DTs provide a virtual representation of physical entities, allowing for accurate monitoring and analysis. The proposed system enhances the resilience of networked systems against adversarial attacks, ensuring dependable and secure communication among devices and infrastructure. This methodology can be applied to applications like vehicular networks, enhancing their robustness and security in adversarial conditions. The effectiveness of the proposed system is demonstrated through real-world experiments and simulations, showcasing its potential for enhancing the security and performance of vehicular networks in dynamic environments in the 6G era.