# Edge Federated Optimization for Heterogeneous Data
###### Abstract
However, these methods may consume additional computation and communication resources for both model training and testing. This work applies an algorithm-based approach and focuses on exploring how to address the problem of data heterogeneity in federated learning by leveraging the FedProx algorithm [22] along with advanced optimization techniques. The FedProx algorithm effectively handles heterogeneous datasets by introducing additional local update steps, thereby enhancing the model's generalization ability across different data distributions. The research also focuses on the feasibility of implementing federated learning models in resource-constrained environments, utilizing the Nvidia Jetson Nano as the experimental platform. Its balanced performance and computational capabilities make it an ideal choice  
Figure 1: The comparison of (**a**) horizontal and (**b**) vertical federated learning (modified and reproduced from [1]). for edge computing applications. Leveraging the Flower federated learning framework, this study effectively deploys and manages federated learning tasks on the Jetson Nano. Flower, being a flexible and lightweight open-source federated learning framework, is well-suited for running on embedded systems like the Jetson Nano. It enhances the efficiency of coordinating model training across multiple devices while reducing communication costs and computational resource consumption. Additionally, this study delves into model optimization strategies, particularly the application of freezing and pruning techniques. Model freezing involves fixing certain layers in a deep learning model, reducing the computational burden significantly, which is especially important for devices with limited computational capabilities. Concurrently, pruning techniques, by removing unimportant weights or neurons from the model, help reduce model size and improve operational efficiency. These techniques not only enhance model performance on devices like the Jetson Nano but also boost the efficiency and scalability of the entire federated learning system. The research in this work covers several key areas:  
* Exploration of sensible practices for implementing federated learning on the Jetson Nano. * Evaluation of the performance of the FedProx algorithm in handling heterogeneous data. * Model optimization involves pruning and layer freezing techniques for improving accuracy and efficiency. This research aims to make contributions to the field of federated learning by providing empirical studies on how to effectively utilize FedProx in a heterogeneous data environment. It also explores and demonstrates the feasibility and efficiency of deploying complex machine learning models on resource-constrained edge computing devices. The structure of this work is as follows: Section 2 reviews recent works related to federated learning in the context of the Internet of Things (IoT). Section 3 discusses the key technologies used in this study. Section 4 describes the system architecture and optimization methods employed. Section 5 comprehensively discusses the experimental results. Finally, Section 6 summarizes the findings of this research and explores future research directions.