Federated Learning for Privacy-Preserving Intelligent Systems
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Abstract
The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.
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References
[1] B. McMahan et al., “Communication-efficient learning of deep networks from decentralized data,” in Proc. AISTATS, 2017.
[2] J. Konečný et al., “Federated optimization: Distributed machine learning for on-device intelligence,” 2016, arXiv preprint arXiv:1610.02527.
[3] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50–60, 2020.
[4] K. Bonawitz et al., “Practical secure aggregation for privacy-preserving machine learning,” in Proc. ACM CCS, 2017.
[5] R. C. Geyer, T. Klein, and M. Nabi, “Differentially private federated learning: A client-level perspective,” 2018, arXiv preprint arXiv:1712.07557.
[6] V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar, “Federated multi-task learning,” in Proc. NeurIPS, 2017.
[7] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, pp. 1–19, 2019.
[8] M. J. Sheller et al., “Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data,” Sci. Rep., vol. 10, no. 1, 2020.
[9] N. Rieke et al., “The future of digital health with federated learning,” NPJ Digital Medicine, vol. 3, no. 1, 2020.
[10] C. Briggs et al., “Federated learning for IoT: Applications, challenges, and opportunities,” 2020, arXiv preprint arXiv:2004.11794.
[11] X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of federated optimization in heterogeneous networks,” in Proc. MLSys, 2020.
[12] S. P. Karimireddy et al., “SCAFFOLD: Stochastic controlled averaging for federated learning,” in Proc. ICML, 2020.
[13] P. Kairouz et al., “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021.
[14] H. Zhu et al., “Deep federated learning: A survey,” 2021, arXiv preprint arXiv:2101.03953.
[15] B. Liu et al., “A survey on federated learning: Principles, applications, and challenges,” 2023, arXiv preprint arXiv:2301.01299.
[16] Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-IID data,” 2018, arXiv preprint arXiv:1806.00582.
[17] T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources,” in Proc. ICC, 2019.
[18] K. Bonawitz et al., “Towards federated learning at scale: System design,” in Proc. MLSys, 2019.
[19] S. Wang et al., “Adaptive federated learning in resource constrained edge computing systems,” IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1205–1221, 2019.
[20] M. Chen, Y. Hao, Y. Li, C.-F. Lai, and D. Wu, “On the computation offloading at ad hoc cloudlet: Architecture and service modes,” IEEE Commun. Mag., vol. 56, no. 6, pp. 18–24, 2018.
[21] A. Hard et al., “Federated learning for mobile keyboard prediction,” 2018, arXiv preprint arXiv:1811.03604.
[22] S. Truex et al., “A hybrid approach to privacy-preserving federated learning,” in Proc. AISec, 2019.
[23] Q. Yang, Y. Liu, and T. Chen, “Federated learning with privacy and security,” IEEE Internet Comput., vol. 24, no. 5, pp. 39–47, 2020.
[24] J. Xu et al., “Federated learning optimization: A survey,” 2021, arXiv preprint arXiv:2104.01331.
[25] C. Zhang et al., “Personalized federated learning: A meta-learning approach,” IEEE Trans. Neural Netw. Learn. Syst., 2022.