Zero Trust Networks and Federated Unlearning Based <scp>6G</scp> Edge Networks: Attack Scenario, Security Model and Future Directions

ABSTRACT The dynamic interplay between federated learning (FL) and federated unlearning (FU) introduces vulnerabilities, particularly the slow poisoning attack scenario by malicious adversaries. The attack proceeds where adversaries can gradually degrade global model performance over successive update cycles. In this letter, we propose a blueprint architecture that integrates zero trust networks (ZTNs) into both the unlearning (FU) request and the client admission (FL) stages to counteract these threats. By enforcing continuous client verification and rigorous risk assessment, our vision ensures that only authenticated and reliable updates contribute to the global model, thereby preserving model integrity and safeguarding sensitive data. Promising future research directions and open challenges are also discussed.