Energy-Aware Adaptive Federated Learning for IoT Security in 6G

Artificial intelligence (AI) and machine learning (ML) are widely adopted in sixth generation (6G) mobile networks. However, the deployment of AI in communication networks will require huge amounts of resources, such as computing, memory, bandwidth, and, as a result, energy. Certain use cases that are associated with resource-constrained devices, for instance, the internet of things (IoT), necessitate designing resource-aware and adaptable AI/ML techniques. In this article, a decentralized energy-aware federated learning (FL) model is proposed for IoT devices that allows the deployment of AI-based cybersecurity operations in 6G. We employ an ordered dropout (OD) mechanism to construct nested submodels from a larger neural network (NN), enabling dynamic adaptation to the energy availability of the system and reducing the overall energy footprint. The experimental evaluations show that the proposed energy-aware model extends the operational lifetime of the deployment framework from 82% to 135% for different datasets, while reducing inference time per sample by up to 50% for the smallest submodel.