# Improved Asynchronous Federated Learning for Data Injection Pollution
## 1 Introduction
In the realm of traffic control, the advent of intelligent transportation systems has brought about a new era of traffic management [15]. Traffic signal control (TSC) is a crucial aspect of this, aiming to mitigate congestion, reduce travel time, and cut down on emissions and energy consumption. Reinforcement learning (RL) has been a primary technique for TSC. However, traditional centralized learning models face significant communication and computing bottlenecks, while distributed learning struggles to adapt across different intersections. As proposed by [PERSON] et al. [16], federated learning provides a novel solution. It integrates the knowledge of local agents into a global model, overcoming the variations among intersections through a unified agent state structure. The model aggregates a segment of the RL neural network to the cloud, and the remaining layers undergo fine-tuning during the convergence of the training process. Experiments have demonstrated a reduction in queuing and waiting times globally, and the model's scalability has been validated on a real-world traffic network in Monaco. In the field of image processing, federated learning has also shown great potential. With the increasing generation of image data from diverse sources, such as surveillance cameras, medical imaging devices, and mobile applications, data privacy becomes a major concern. Federated learning allows for edge devices or local servers to train models on their local data without sharing the raw data, thus safeguarding privacy. As elaborated in the review paper \"A review on federated learning towards image processing\" by [PERSON] et al. (2017), federated learning in image processing can be applied to various tasks like image recognition, segmentation, and classification. For example, in medical image segmentation across multiple healthcare centers, each center can train a model on its local patient data, and then the model parameters are aggregated to build a global model that can be beneficial for all participating centers while protecting patient privacy ([PERSON] et al., 2017). The applications of federated learning in traffic control and image processing are still in the process of continuous exploration and development. As technology advances, more innovative applications and optimized algorithms are expected to emerge, further enhancing the efficiency and effectiveness of these two important fields. Although asynchronous federal learning has made remarkable progress in recent years, there are still some challenges, as follows ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017):  
(1) Communication efficiency: The asynchronous method can lead to conflicts between parameter updates on the server side. Recent studies such as ([PERSON] et al., 2017) have explored dynamic communication scheduling to mitigate such conflicts. However, how to effectively and orderly coordinate communication between the client and the server in heterogeneous environments remains an open problem that must urgently be solved. (2) Convergence guarantee: Most existing studies are based on experimental verification and lack rigorous theoretical analysis and proof of convergence. (3) Robustness: Malicious attacks in the asynchronous environment (such as model poisoning) may have a serious impact on the global model. Recent work ([PERSON] et al., 2017) proposed the use of gradient anomaly detection frameworks to filter poison updates, but the integration of such methods into AFL requires further investigation. How to effectively improve the anti-attack ability of the system remains an important direction of future research. In general, AFL, a flexible and efficient distributed learning method, shows great potential in the protection of privacy and in scenarios with limited computing resources. With the development of theoretical research and technical practice, its performance in practical applications will improve.