Towards a Dynamic Gradient Evaluation Strategy-based Federative Learning model

Federated Learning (FL) has emerged as a promising approach for training machine learning models on decentralized data, but it poses significant challenges in terms of model optimization and performance evaluation. To address these challenges, this paper introduces an application generator/tailoring tool designed specifically for FL analysts. The tool provides a framework for configuring Federated Averaging algorithms and tailoring them to suit a related set of applications, such as image recognition. The application generator/tailoring tool employs a dynamic gradient evaluation strategy to evaluate the performance of the FL model at runtime. This approach enables the analyst to adjust the FL model's behavior to achieve the desired objectives in terms of communication costs and data convergence requirements. By providing such flexibility, the tool empowers analysts to optimize FL models according to their specific needs and priorities. To demonstrate the effectiveness of the application generator/tailoring tool, the authors conducted experiments with traffic sign images. The results showed that the tool is highly effective in improving the performance of FL models for image recognition tasks. This finding suggests that the tool has significant potential to accelerate the development of FL models and enhance their performance in real-world applications. Overall, the application generator/tailoring tool presented in this paper provides a powerful and flexible approach to FL model optimization and evaluation. By enabling analysts to dynamically evaluate and optimize FL models, the tool has the potential to accelerate the adoption of FL in a wide range of real-world applications, from healthcare to transportation and beyond. Impact Statement — This paper presents a novel application generator/tailoring tool that allows Federated Learning analysts to dynamically evaluate and optimize FL models for image recognition tasks, while balancing communication costs and data convergence requirements. The tool's effectiveness is demonstrated through experiments with traffic sign images, highlighting its potential to accelerate FL development and improve performance in real-world applications.