Federated Learning Frameworks for Privacy-Preserving Smart Applications

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PI6F6C

    Published 03-05-2024

  • Federated Learning, Privacy Preservation, Smart Applications, Edge Computing, Artificial Intelligence, Internet Of Things, Distributed Machine Learning, Secure Aggregation, Differential Privacy, Blockchain, Explainable AI, Secure Edge Intelligence

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    N. Seshagiri, “Federated Learning Frameworks for Privacy-Preserving Smart Applications”, ijmiet, vol. 7, no. 1, pp. 01–19, Mar. 2024, doi: 10.67228/30715628/IJMIET-2024PI6F6C.

  • Abstract

    Internet of Things (IoT), edge computing, and cloud computing has transformed data-driven services across healthcare, transportation, manufacturing, finance, agriculture, and smart cities. These applications generate large volumes of sensitive distributed data, making traditional centralized machine learning unsuitable due to privacy, security, communication, and regulatory challenges. Federated Learning (FL) addresses these issues by enabling collaborative model training without transferring raw data, thereby preserving user privacy.This paper presents a privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, encryption, adaptive communication, and federated optimization for large-scale heterogeneous environments. The framework incorporates edge computing, blockchain, Trusted Execution Environments (TEE), and Explainable AI (XAI) to improve security, transparency, and trust. A multi-layer architecture consisting of client devices, edge servers, federated coordinators, cloud services, and security modules is proposed. Adaptive client selection, weighted federated averaging, dynamic privacy allocation, and asynchronous synchronization are employed to improve learning under Non-IID data distributions. Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.

  • References

    [1] H. Brendan McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), pp. 1273–1282, 2017.

    [2] Jakub Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, "Federated Learning: Strategies for Improving Communication Efficiency," Proc. NIPS Workshop on Private Multi-Party Machine Learning, 2016.

    [3] Tian Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, "Federated Optimization in Heterogeneous Networks," Proc. Machine Learning and Systems (MLSys), vol. 2, pp. 429–450, 2020.

    [4] Sai Praneeth Karimireddy, S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, "SCAFFOLD: Stochastic Controlled Averaging for Federated Learning," Proc. 37th Int. Conf. Machine Learning (ICML), pp. 5132–5143, 2020.

    [5] Jianyu Wang, Q. Liu, H. Liang, G. Joshi, and H. Vincent Poor, "Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization," Advances in Neural Information Processing Systems, vol. 33, pp. 7611–7623, 2020.

    [6] Virat Shejwalkar and A. Houmansadr, "MANDA: Model-Agnostic Defense Against Byzantine Attacks in Federated Learning," IEEE Journal on Selected Areas in Communications, vol. 39, no. 7, pp. 2155–2168, 2021.

    [7] Cynthia Dwork and A. Roth, The Algorithmic Foundations of Differential Privacy. Boston, MA, USA: Now Publishers, 2014.

    [8] Keith Bonawitz et al., "Practical Secure Aggregation for Privacy-Preserving Machine Learning," Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), pp. 1175–1191, 2017.

    [9] Pascal Paillier, "Public-Key Cryptosystems Based on Composite Degree Residuosity Classes," Advances in Cryptology (EUROCRYPT), pp. 223–238, 1999.

    [10] Qiang Yang, Y. Liu, T. Chen, and Y. Tong, "Federated Machine Learning: Concept and Applications," ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, 2019.

    [11] Peter 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.

    [12] Jakub Konečný, Q. Yang, A. Li, W. Yu, and M. Yao, "Blockchain-Based Federated Learning: A Comprehensive Survey," IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 1222–1248, 2022.

    [13] Yue Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, "Federated Learning with Non-IID Data," arXiv preprint arXiv:1806.00582, 2018.

    [14] Brendan McMahan, D. Ramage, K. Talwar, and L. Zhang, "Learning Differentially Private Recurrent Language Models," International Conference on Learning Representations (ICLR), 2018.

    [15] Jakub Konečný, V. Smith, T. Li, and A. Talwalkar, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 39, no. 3, pp. 50–60, 2022.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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