A trusted decision fusion approach for the power internet of things with federated learning

Abstract

The power Internet of Things generates a large amount of data at any time, which can be transformed into precise decisions with the help of artificial intelligence approaches. However, the owners of electricity data with boundaries are often concerned with data leakage. Therefore, when building models that feed big data into deep learning artificial intelligence approaches for precise decision-making within the power Internet of Things, it is essential to ensure the data’s security. This paper proposes a framework for model training and decision making system applied to the field of power IoT, which consists of two parts: data security sharing and hierarchical decision making. The proposed framework utilizes a homomorphic encryption-based federated learning approach to protect private data from leakage. In addition, data augmentation and transfer learning are used to address the issue of insufficient local training data. Moreover, the framework attempts to incorporate the specialized nature of traditional manual decision-making in the power field by fusing expert and model values after stratifying the requirements. Experiments are conducted to simulate the decision requirements in the field of power Internet of Things (e.g., electrical material identification), using image recognition as an example. The experimental results show that the proposed models can achieve high accuracy rates and the fusion approach is feasible.