Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the `sign' of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional Fed-SGD models.

翻译：智能电表测量数据虽对精确需求预测至关重要，却面临消费者隐私泄露、数据安全等问题。近期文献将联邦学习作为一种有前景的隐私保护机器学习替代方案进行探索，该方案无需暴露原始数据即可实现短期负荷预测的模型协同训练。尽管具有优势，标准联邦学习仍易遭受一种被称为拜占庭攻击的棘手网络威胁——该攻击由故障或恶意客户端发起。为此，为提升联邦短期负荷预测对拜占庭威胁的鲁棒性，我们开发了一种基于差分隐私的最先进安全联邦学习框架，该框架在保障单个智能电表数据隐私的同时，兼顾联邦学习模型与架构的安全性。所提框架利用符号随机梯度下降算法中的梯度量化思想，客户端在本地模型训练后仅向控制中心传输梯度的"符号"。通过采用基准神经网络及一组拜占庭攻击模型进行的实验表明，我们的方法能有效缓解此类威胁，从而显著优于传统联邦随机梯度下降模型。