## 1 Introduction  
With the rapid development of IoT technology, IoT devices have provided ubiquitous sensing and computing powers and are widely used in many scenarios such as smart cities, healthcare, smart homes, and smart industries. According to a report by IoT Analytics [1], by 2027, there will be 29 billion IoT devices working concurrently around the world, compared to just 14.4 billion in 2022. The vast number of data generated by a multitude of devices provides rich material for analysis by artificial intelligence technologies such as machine learning and deep learning. These technologies have demonstrated powerful analytical and mining capabilities in fields like image classification, speech recognition, and natural language processing, effectively leveraging the data collected by the Internet of Things to uncover hidden patterns and information. Traditional machine learning methods rely on centralized AI frameworks located in data centers or clouds for data analysis and model training, which require centralized collection of data dispersed in various locations and model training in data centers. However, centralized machine learning methods also raise concerns about data privacy, as the data may contain sensitive information such as users' personal preferences, health status, and more. Owing to concerns over privacy protection, users are often reluctant to share the data they possess, leading to data silos that prevent effective centralization for training and analysis. Therefore, a new machine learning training method is needed to alleviate users' concerns about privacy issues. In 2016, Google researchers introduced a technique known as federated learning [2], which is a form of distributed machine learning aimed at addressing the issue of machine learning across a range of mobile devices. Federated learning, as opposed to the more traditional centralized machine learning, does away with the requirement to centralize data within a data center. Under the coordination of a server, various IoT devices act as clients to collaboratively train a model. Initially, the server establishes the current global model. Subsequently, each client downloads this model, utilizes its own data to train a local model, and then uploads the outcomes back to the server. The server then amalgamates these local models to form a new global model, which is subsequently downloaded by the clients to train their new local models. This cycle is repeated until a predefined training goal is met. By maintaining data privacy at the local level, this approach to machine learning ensures data privacy and security, potentially alleviating users' privacy concerns. It can enhance user privacy as the server is only aware of the model, not the learning data themselves. As a result, federated learning has been the subject of extensive research and application in areas such as health care [3], financial services [4], and intelligent manufacturing [5], emerging as a highly promising research field within artificial intelligence. However, existing research indicates that traditional machine learning can be outperformed by federated learning in better protecting users' private data. In the domain of federated learning, there are still multiple attack methods that can threaten users' private data. Through their research, [PERSON] et al. [6] discovered that the local models submitted by users contain some information, and it is possible to infer sensitive information, such as users' training data, by analyzing the output of these local models. A framework was proposed by [PERSON] et al. [7] that integrates a GAN with a multi-task discriminator on a malicious FL server to reconstruct a user's private training data. On the other hand, [PERSON] et al. [8] suggested a method where by constructing fake data and labels for training, the resulting fake model is maximized to differ as much as possible from the user-submitted local model, and through iterative updates, the user's training data can ultimately be recovered.