# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 1 Introduction  
Over the past few years, the global economy has undergone a substantial period of rapid development. However, this growth has been accompanied by increasing energy demands, expanding resource supplies, and intensifying environmental pollution [1]. In response to these pressing challenges, the power Internet of Things (PIoT) technology has been steadily addressing existing issues. PIoT capitalizes on its sensitive transmission capability, real-time communication prowess, intelligent control capacity, and robust information security capability. As a result, it seamlessly integrates traditional Internet of Things (IoT) concepts and transforms into a cutting-edge system for efficiently operating and managing power systems [2, 3]. This concept fully utilizes the mature advantages of IoT technology in areas such as smart sensors, wide-area communication, and edge computing. Resource coordination is accomplished via intelligent network control and grid distribution. The application of PIoT technology spans across diverse sectors, encompassing industrial production, smart cities, agricultural production, environmental monitoring, security surveillance, and home automation, achieving significant economic benefits [4]. However, PIoT also faces security issues. Due to its structure and operational characteristics, it demands high-security protection to ensure the confidentiality, integrity, and availability ofdata, meeting the requirements of information security. It is more complex than conventional IoT as it involves a greater variety of devices and denser node deployment. Therefore, the overall security risks of PIoT are also higher. Additionally, the health condition of power equipment is a crucial factor in ensuring the secure and reliable operation of the power system [5]. In the power grid, the interconnection between terminal devices and edge nodes allows for the comprehensive and flexible perception of electricity consumption status, enabling intelligent sensing of real-time operational conditions and digitalized maintenance. This facilitates the efficient processing of collected information and prompt intelligent decision-making. For power companies, important tasks encompass monitoring and the intelligent management of power equipment status, and key aspects include the collection, storage, and processing of large amounts of power equipment data [6]. Therefore, applying machine learning technology to the management, analysis, and decision-making of power equipment data can effectively enhance the efficiency of related work in PIoT. Existing applications of machine learning and PIoT typically involve training large-scale machine learning models by uploading local data from edge nodes to cloud servers. Data management, analysis, and decision-making are conducted through centralized approaches based on cloud storage [7]. In traditional power systems, direct data transmission occurs between edge nodes and cloud servers. However, this method of data transmission is vulnerable to attacks by malicious third parties, resulting in data breaches that compromise the security of power data and lead to significant power incidents [8]. Moreover, future trends will require real-time data privacy and security to reduce the risk of data leaks and enhance the overall reliability of the power grid. Centralized data centers no longer suffice in addressing the confidentiality, authenticity, integrity, and reliability requirements of the information security of power IoT data. Federated learning, as a machine learning approach, involves multiple data owners and a central server collaborating to train models [9]. It enables the maintenance of multiple distributed training datasets simultaneously. Federated learning offers the opportunity to employ more sophisticated models while simultaneously safeguarding privacy and the security of local data. It facilitates the collaborative training of machine learning models among several data owners without the necessity of transmitting data to a central server.