Sustainable Federated Learning in 6G Networks: The Case for Cybersecurity

6G networks are envisaged to be AI-native and employ distributed AI at various layers, which would enable **Federated Learning (FL)** as an integral technology. Despite the significant benefits, the **carbon footprint** of FL in such complex systems warrants deeper inspection and optimization as part of sustainability concerns. Moreover, as next-generation networks face emerging threat vectors, **cybersecurity** plays a crucial role in defending against such attacks. This study integrates the concepts of FL, sustainability, and cybersecurity in the context of future networks, and presents a use-case of intrusion detection on a **Virtual Network Function (VNF)** dataset. In the scope of the study, this capsule provides a codebase for conducting _carbon-aware_ FL experiments using the **Flower** framework, where each client employs **CodeCarbon** to measure and track their carbon footprint during the local training phase of an FL pipeline. By using FL, a global intrusion detection model is trained and evaluated on VNF traffic. Experiments are conducted across different _carbon-aware client selection_ mechanisms, and the results are compared based on the intrusion detection model performance and the carbon footprint of the training process.