Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anomaly detection have achieved great success, but are typically centralized, involving sharing local data with a central server which raises privacy and security concerns. Federated Learning (FL) has been gaining popularity as it enables distributed learning without sharing local data. However, FL depends on neural networks, which are vulnerable to adversarial attacks that manipulate data, leading models to make erroneous predictions. While adversarial attacks have been explored in the image domain, they remain largely unexplored in time series problems, especially in the energy domain. Moreover, the effect of adversarial attacks in the FL setting is also mostly unknown. This paper assesses the vulnerability of FL-based anomaly detection in energy data to adversarial attacks. Specifically, two state-of-the-art models, Long Short Term Memory (LSTM) and Transformers, are used to detect anomalies in an FL setting, and two white-box attack methods, Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), are employed to perturb the data. The results show that FL is more sensitive to PGD attacks than to FGSM attacks, attributed to PGD's iterative nature, resulting in an accuracy drop of over 10% even with naive, weaker attacks. Moreover, FL is more affected by these attacks than centralized learning, highlighting the need for defense mechanisms in FL.

翻译：异常检测在能源领域至关重要，用于识别指示设备故障、能源盗窃或其他问题的异常模式。用于异常检测的机器学习技术已取得巨大成功，但通常是集中式的，涉及将本地数据共享给中央服务器，这引发了隐私和安全问题。联邦学习（FL）因其能够在不共享本地数据的情况下实现分布式学习而日益受到欢迎。然而，FL依赖于神经网络，而神经网络容易受到操纵数据的对抗性攻击，导致模型做出错误预测。尽管对抗性攻击在图像领域已得到广泛研究，但在时间序列问题中，尤其是在能源领域，其研究仍相对不足。此外，对抗性攻击在FL环境中的影响也大多未知。本文评估了基于FL的能源数据异常检测对对抗性攻击的脆弱性。具体而言，我们使用两种最先进的模型——长短期记忆网络（LSTM）和Transformer，在FL环境中检测异常，并采用两种白盒攻击方法——快速梯度符号法（FGSM）和投影梯度下降法（PGD）来扰动数据。结果表明，FL对PGD攻击比FGSM攻击更为敏感，这归因于PGD的迭代性质，即使使用简单、较弱的攻击，也会导致准确率下降超过10%。此外，与集中式学习相比，FL受这些攻击的影响更大，这凸显了在FL中部署防御机制的必要性。