# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 1 Introduction  
In the process of completing large-scale, fine-grained sensing tasks, the multidimensional and massive amounts of sensing data collected by the new generation of crowds-sensing systems can support intelligent applications, such as smart community services; environmental monitoring; intelligent transportation; and navigation through effective processing, monitoring, analysis, inference, and decision making using artificial intelligence [1; 2; 3]. Mobile crowd sensing is an open system based on smart mobile devices, where numerous mobile terminal devices collaborate to collect and preprocess a large amount of information. These data are then uploaded to backend servers for further processing through various connectivity methods, such as 3G/4G/5G cellular networks, Wi-Fi, and Bluetooth [4]. Typically, these data contain sensitive information about end users, such as their personal identity, bank account details, and service access records. The wireless communication, collaborative information processing, and centralized storage of user data inbackend servers in this open environment make mobile crowd-sensing systems vulnerable to attacks [5; 6], presenting challenges related to user data security and privacy. Federated learning (FL) [7] is a distributed machine learning technique that involves training a shared artificial intelligence model collaboratively across multiple devices. During this process, training data (or private data) remain stored locally on the devices, with local models being uploaded to a server for the aggregation of all models [8; 9]. In federated learning, training data are kept locally, and the central server receives gradient information to update the global model. Data collection, storage, and server training are separated, thereby ensuring the security of private data. Introducing federated learning into crowdsensing applications becomes particularly crucial in addressing privacy concerns that arise from user data uploads [10; 11]. Applying FL to crowd-sensing systems can enhance the data security, but it also introduces the risk of malicious users launching model extraction attacks and model reverse engineering attacks, leading to privacy breaches [12]. This paper proposes a method for securing user data in crowd-sensing applications based on federated learning named FedCrow. First, we constructed an FL framework tailored for crowd-sensing applications. Next, an appropriate encryption method was selected based on the crowdsensing application scenario to ensure data security during transmission, where the goal was to build a secure federated-learning framework. Subsequently, malicious users were identified and filtered out using a user contribution level to minimize potential harm they may cause. Finally, through experiments, the security of the federated-learning framework based on credibility scores for legitimate user authentication was validated in terms of aspects such as resisting data poisoning, model attacks, inference attacks, and differential privacy attacks. The security of the transmitted model data was also validated through experiments by selecting suitable encryption methods based on the size of the data during the reference model training. The main contributions of this work are summarized as follows:  
* We propose a federated-learning-based framework for protecting user data in crowdsensing applications called FedCrow. It utilizes federated learning to locally store sensing data on terminal devices, exchanges only gradient parameters between terminals and servers, and trains models among multiple terminal users in crowd-sensing applications. Additionally, it employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thus constructing a secure federated-learning framework tailored for crowd-sensing applications.