In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load data can inadvertently reveal the daily routines of residential users, thereby posing a risk to their property security. While federated learning (FL) has been employed to safeguard user privacy by enabling model training without the exchange of raw data, these FL models have shown vulnerabilities to emerging attack techniques, such as Deep Leakage from Gradients and poisoning attacks. To counteract these, we initially employ a Secure-Aggregation (SecAgg) algorithm that leverages multiparty computation cryptographic techniques to mitigate the risk of gradient leakage. However, the introduction of SecAgg necessitates the deployment of additional sub-center servers for executing the multiparty computation protocol, thereby escalating computational complexity and reducing system robustness, especially in scenarios where one or more sub-centers are unavailable. To address these challenges, we introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. The Distributed Markovian Switching (DMS) topology shows strong robustness towards the poisoning attacks as well. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm. It not only significantly minimizes communication complexity but also maintains accuracy levels comparable to traditional FL methods, thereby enhancing the scalability of our load forecasting algorithm.

翻译：在电力系统领域，居民用户日益参与负荷预测应用加剧了对数据隐私的担忧。具体而言，负荷数据可能无意中泄露居民用户的日常行为规律，从而对其财产安全构成风险。虽然联邦学习（FL）已通过在不交换原始数据的情况下实现模型训练来保障用户隐私，但这些FL模型已显示出对新兴攻击技术（如梯度深度泄露和投毒攻击）的脆弱性。为应对这些威胁，我们首先采用基于安全聚合（SecAgg）算法，该算法利用多方计算密码技术来降低梯度泄露风险。然而，引入SecAgg需要部署额外的子中心服务器来执行多方计算协议，从而增加了计算复杂度并降低了系统鲁棒性，尤其是在一个或多个子中心不可用的情况下。为解决这些挑战，我们提出了一种基于马尔可夫切换的分布式训练框架，并通过严格的理论分析证明了其收敛性。分布式马尔可夫切换（DMS）拓扑结构对投毒攻击同样表现出强鲁棒性。采用真实电力系统负荷数据的案例研究验证了我们所提算法的有效性。该算法不仅显著降低了通信复杂度，还能保持与传统FL方法相当的精度水平，从而增强了负荷预测算法的可扩展性。