Novel Stealth Communication Round Attack and Robust Incentivized Federated Averaging for Load Forecasting

Abstract

Federated learning (FL) has gained prominence in energy forecasting applications. Despite its advantages, FL remains vulnerable to adversarial attacks that threaten the reliability of predictive models. This study introduces a stealth attack, Federated Communication Round Attack (Fed-CRA), which increases communication rounds without affecting forecasting accuracy. Increased communication rounds can delay decision-making, reducing system responsiveness and cost-effectiveness in dynamic energy forecasting scenarios. Experimental validation on two datasets demonstrated that Fed-CRA increased communication rounds by 574% (from 72 to 485) in the AEP dataset and by 237% (from 92 to 310) in the COMED dataset. This led to a corresponding rise in energy consumption by 573% (from 41.04 kWh to 276.35 kWh) and 237% (from 52.44 kWh to 176.65 kWh), respectively, while preserving forecasting accuracy. To counter this attack, we proposed Federated Incentivized Averaging (Fed-InA), a game theory-inspired framework that rewards honest clients and penalizes dishonest ones based on their contributions. Results showed that Fed-InA reduced the additional communication rounds caused by Fed-CRA by 85% in the AEP dataset and 70% in the COMED dataset, while maintaining forecasting performance. Fed-InA achieves resource efficiency comparable to Federated Averaging (FedAvg) and demonstrates robustness in handling non-IID data.