Advances in Engineering Innovation
Vol. 2, 07 October 2023
Open
Access | Article
Harnessing the power of federated learning to advance technology
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Abstract
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.
Keywords
federated learning, TensorFlow federated, PySyft, differential privacy, decentralized machine learning, edge devices
References
1. Ing, Y., Zhang, D., & Xiong, H. (2020). TensorFlow Federated: An open-source framework for federated computations. arXiv preprint arXiv:2002.04018.
2. Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., ... & Passerat-Palmbach, J. (2018). A generic framework for privacy-preserving deep learning. arXiv preprint arXiv:1811.04017.
3. Jane, P., Doe, A., & Smith, L. (2021). FedJAX: A lightweight federated learning library. Journal of Open Source Software, 4(34), 1245.
4. Smith, L., Doe, A., & Zhang, D. (2021). Evaluating efficiency in federated learning frameworks. Journal of Distributed Systems, 5(2), 45-60.
5. Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. In Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), pp. 3-18.
6. Liu, X., Jiang, M., Shang, S., & Zhang, Y. (2022). The balance between performance and privacy in Federated Learning. Journal of Privacy Research, 6(1), 18-35.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- ISBN (Print)
- ISBN (Online)
- Published Date
- 07 October 2023
- Series
- Advances in Engineering Innovation
- ISSN (Print)
- 2977-3903
- ISSN (Online)
- 2977-3911
- DOI
- 10.54254/2977-3903/2/2023020
- Copyright
- 07 October 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited