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Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed Machine Learning (ML), enabling model training across distributed devices without compromising data privacy. However, the impact of hyper-parameters on FL model performance remains understudied and most of the existing FL studies rely on default or out-of-the-box hyper-parameters, often leading to suboptimal convergence. This study specifically investigates the intricate relationship between key hyper-parameters—learning rate, epochs per round, batch size, and client participation ratio (CPR)—and the performance of FL models on two distinct datasets: CIFAR-10 using ResNet-18 and FashionMNIST using a simple CNN model. Through systematic exploration on these datasets, employing a centralized server and 200 clients, we elucidate the significant impact of varying hyper-parameters. Our findings underscore the importance of dataset-specific hyper-parameter optimization, revealing contrasting optimal configurations for the complex CIFAR-10 dataset and the simpler FashionMNIST dataset. Additionally, the correlation analysis offers a deep understanding of hyper-parameter inter-dependencies, essential for effective optimization. This study provides valuable insights for practitioners to customize hyper-parameter configurations, ensuring optimal performance for FL models trained on different types of datasets and provides a foundation for future exploration in hyper-parameter optimization within the FL domain. |
