Federated Learning Scheme for Environmental Monitoring Based on Edge Computing

Aiming at the problems of unbalanced edge device resources, communication delay and low model quality in the field of environmental monitoring, this paper proposes an adaptive federated learning algorithm for environmental monitoring based on edge computing. This algorithm aims to use edge devices for data processing, and according to each the resource limitation of the device adjusts the aggregation frequency of the global model to better adapt to different monitoring environments. By considering the resource differences between edge devices, the algorithm adopts a strategy of dynamically optimizing the iteration frequency to improve the training effect of the model. Compared with the traditional fixed iteration frequency, the adjustment strategy of this algorithm is more flexible and can better adapt to different data distribution and participant characteristics. Through a large number of experimental evaluations, and using the same algorithm convolutional neural networks-federated learning (CNN-FL), federated averaging (FedAvg) and hierarchical federated edge learning (HFEL), the algorithm proposed in this paper has significant advantages in algorithm performance and economic cost. This algorithm provides an efficient, safe and reliable method for environmental monitoring. Expanded approach to data analysis and modeling to help drive improvements in environmental monitoring capabilities. © 2023 Chinese Institute of Electronics. All rights reserved.