Resilient Federated Learning Against Injection and Evasion Attacks in Edge Smart Grids: A Simulation Study

The modernization of smart grids through edge computing introduces significant cybersecurity challenges, most stemming from adversarial machine learning attacks that compromise distributed intelligence. Although Federated Learning is an appealing decentralized model training paradigm for edge smart grids, its resilience against coordinated injection and evasion attacks has not yet been thoroughly explored. To address this critical gap, we develop and evaluate a resilient Federated Learning model for edge-based innovative grid applications. Under a rigorous simulation-based experimental design, we created a controlled environment based on synthetic energy-demand data and implemented adversarial attack scenarios to ensure model robustness. We propose a resilience enhancement layer in our framework during the federated aggregation process to curtail malicious model updates and adversarial inferences. The results show significant improvement in the stability of the proposed model under attack, maintaining a robustness index above 0.62, whereas baseline approaches exhibit complete degradation. This corresponds to a reduction of approximately 34% in the attack impact rate across different-intensity attack scenarios, while maintaining high stability in aggregation. In addition to the adversarial testing framework in the domain of Federated Learning, this work provides a validated resilience model that secures analytics of smart grids without requiring access to raw data. Our methodology presents a resource-efficient alternative to physical testing and enables safe yet comprehensive security evaluation in critical infrastructure applications.