The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs. Our approach reveals latent interconnections and employs personalized encoding methods to capture unique insights, understanding the relationships between features and optimal strategies, allowing our model to generalize to previously unseen data. Experimental results validate the robustness of our approach, affirming its proficiency in effectively learning policy models for smart grid challenges.

翻译：智能电网领域需要强化现有能源管理系统的能力；联邦学习（FL）与此目标高度契合，因其能够在保持数据隐私的前提下，基于异构数据集训练模型，这使得它适用于智能电网应用场景——这些场景通常涉及非均匀数据分布及特征间的相互依赖关系，从而限制了线性模型的适用性。本文提出一种融合联邦学习与信任域策略优化（FL TRPO）的框架，旨在降低与能源相关的排放和成本。我们的方法揭示了潜在的内在关联，并采用个性化编码方法捕捉独特见解，理解特征与最优策略之间的关系，从而使模型能够泛化至未见过的数据。实验结果验证了该方法的鲁棒性，证实了其在有效学习智能电网挑战的策略模型方面的卓越能力。