As sixth-generation (6 G) networks evolve towards AI-native architectures, Federated Learning (FL) is becoming a cornerstone for enabling intelligent services by leveraging distributed data from diverse sources such as Integrated Sensing and Communication (ISAC) devices and edge clients. However, a critical challenge lies in efficiently handling data removal requests, mandated by regulations like the “right to be forgotten”. This problem is significantly exacerbated by the extreme data heterogeneity ( non-IID) inherent across diverse 6 G devices and the communication constraints of wireless networks. To address these challenges, this paper proposes SeFUL, a novel two-stage federated unlearning framework tailored for the security and privacy demands of 6 G systems. SeFUL first proactively mitigates data heterogeneity by partitioning clients into clusters based on their data distribution similarity. Subsequently, a lightweight, information-theoretic unlearning strategy is deployed. This method surgically erases information by optimizing a composite loss function which, in the latent space, pushes the feature representations of forgotten data away from their original class cluster and towards samples from other classes, while reinforcing knowledge from the retain set. Comprehensive experiments on benchmark datasets demonstrate that SeFUL achieves unlearning performance on par with the gold standard of complete model retraining. It successfully reduces forget-set accuracy to nearly random guess levels while preserving high retain-set accuracy, significantly outperforming existing state-of-the-art methods. Furthermore, Membership Inference Attacks (MIAs) confirm that SeFUL effectively reduces privacy risks to a level statistically indistinguishable from a fully retrained model, validating its efficacy as a robust privacy-preserving mechanism.