Comparative Analysis of FedAvg and FedProx Algorithms in Federated Learning for Handwritten Character Recognition on the EMNIST Dataset

Federated Learning (FL) is a privacy-protecting way to train machine learning models on different devices. This study compares two popular FL algorithms, FedAvg and FedProx, using the EMNIST dataset, which is a more complex version of MNIST for recognizing handwritten letters. FedAvg is a simple and efficient algorithm that combines models trained on local devices. FedProx adds a term to help deal with non-IID data. The results show that both algorithms get similar global accuracy (94%) in an IID data setting. FedAvg has faster and more stable convergence, while FedProx has more ups and downs early on but becomes more stable later. These results show the strengths of both algorithms and suggest that each can work well in different federated learning situations. Overall, this comparison provides useful insights for designing future FL systems that balance efficiency, stability, and adaptability. Future research will test these algorithms in more complex non-IID conditions and with more clients.