## 2 Federated Learning in AQI Prediction
Numerical findings demonstrate that the suggested framework may enable accurate and energy-efficient AQI sensing without compromising the privacy of raw data through comprehensive case studies on a real-world dataset. In the use of smart city sensing, [PERSON], et al. [50] introduces a unique edge computing framework called Federated Compressed Learning (FCL), which enables fast data creation while protecting data privacy for PM2.5 forecasts. The suggested method incorporates safe data transmission while inheriting the fundamental concepts of the compression methodology and regional cooperative learning. As a result, it could decrease the volume of data while maintaining data privacy. By utilizing the FCL edge computing architecture, this study seeks to construct a wireless sensor network system that is powered by green energy. It is also one of the fundamental technologies for the deployment of reconfigurable and customizable sensing devices. As a result, prototypes are created to verify the performance of the suggested framework. The outcomes demonstrate a reduction in data consumption of more than 95% with an error rate of less than 5%. Finally, compared to centralized training, the prediction results based on the FCL will produce somewhat lower accuracy. However, the information might be safely sent and tightly compressed in WSNs. Meanwhile, [PERSON], et al. [51] provide a framework for distributed learning that supports cross-spatial training among participants from cities and prefectures, for example. Convolutional Recurrent Neural Networks (CRNN) are trained locally in each area with the goal of predicting the local Oxidant alert level, while an aggregated global model improves the distilled information from every part of a region. The study shows that although adaptive structure at the predictive component of the deep neural network model may capture various environmental monitoring stations' configurations in local regions, planned common elements of the CRNN can be fused worldwide. In order to enhance the accuracy of the whole FL system, some experiment findings also point to strategies for maintaining a balance between local deep neural network training epochs and synchronous training rounds for FL. The findings also demonstrate that by transferring a shared global model, additional participating locations may train and rapidly achieve optimal local models. For the first time, a conditional GAN imputation technique is proposed in this research inside a FL framework by [PERSON], [PERSON], et al. [52] to resolve data sets from various data ownerswithout sharing them. Additionally, they use the Wasserstein distance and \"Hint mask\" method to enhance the performance of the standard conditional GAN. The outcomes of the experiments demonstrate that their GAN-based imputation techniques may deliver the best results. Additionally, their federated GAN imputation technique performs better than the GAN imputation method that was locally trained for each participant, indicating the viability of their imputation model. By facilitating private multi-institutional partnerships, their suggested federated GAN technique can enhance access to air quality data, hence improving model quality. They further explore how the geographic distribution of data across cooperating participants affects model quality, and, surprisingly, they discover that the GAN training process using a FL framework performs more consistently. A review from [PERSON], [PERSON], et al. [55] imposes a framework on the basis of data from environmental, health, and AQI prediction for integrated environmental and health impact assessment systems, as well as big data learning and prediction abilities of AI and FL. In the framework of early health care service preparedness prediction and hospital administration, it is also crucial for efficient medical services. Finally, [PERSON], [PERSON], et al.