Doctorate Division, Capitol Technology University, United States of America.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1303–1312
Received on 31 July 2024; revised on 08 September 2024; accepted on 10 September 2024
Federated Learning (FL) has emerged as a transformative paradigm in machine learning, advocating for decentralized, privacy-preserving model training. This study provides a comprehensive evaluation of contemporary FL frameworks – TensorFlow Federated (TFF), PySyft, and FedJAX – across three diverse datasets: CIFAR-10, IMDb reviews, and the UCI Heart Disease dataset. Our results demonstrate TFF's superior performance on image classification tasks, while PySyft excels in both efficiency and privacy for textual data. The study underscores the potential of FL in ensuring data privacy and model performance yet emphasizes areas warranting improvement. As the volume of edge devices escalates and the need for data privacy intensifies, refining and expanding FL frameworks become essential for future machine learning deployments.
Federated Learning; TensorFlow Federated; PySyft; Differential Privacy; Decentralized Machine Learning; Edge Devices
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Luay Bahjat Albtosh. Harnessing the power of federated learning to advance technology. World Journal of Advanced Research and Reviews, 2024, 23(3), 1303-1312. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2768