Energy-Efficient Federated Learning with Temporal Convolutional Networks for Intrusion Detection

Abstract

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface of modern network infrastructures, necessitating intelligent and scalable intrusion detection systems. Federated Learning (FL) has emerged as a promising paradigm for distributed model training without centralized data sharing; however, challenges such as energy efficiency, data heterogeneity, and privacy preservation remain inadequately addressed. Existing studies often emphasize optimization objectives theoretically without validating them under realistic constraints. This paper proposes an energy-aware federated learning framework integrating Temporal Convolutional Networks (TCNs) for intrusion detection using distributed network traffic data. The framework incorporates differential privacy for secure model updates and a conceptual energy-aware client participation strategy. Experiments are conducted on the UNSW-NB15 dataset under a controlled setting with fixed client participation and communication parameters. The results demonstrate that the proposed model achieves improved classification accuracy and stable convergence behavior across communication rounds while operating under a fixed energy budget. However, energy consumption remains constant due to controlled experimental conditions, indicating that the study evaluates performance under energy constraints rather than dynamic energy optimization. The findings highlight the effectiveness of TCN-based federated models for intrusion detection in resource-constrained environments. Future work will focus on dynamic energy modeling, heterogeneous client environments, and comprehensive multi-objective evaluation.