Federated Predictive Learning with Privacy-Aware Model Aggregation for Distributed Analytics

  • Authors

    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India Author
    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2025PI6L4G

    Published 01-03-2025

  • Federated Learning, Distributed Analytics, Privacy Preservation, Predictive Learning, Model Aggregation, Secure Machine Learning, Edge Computing, Artificial Intelligence, Distributed Optimization, Federated Averaging, Privacy-Aware Aggregation, Machine Learning Security

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    H. N. Mahabala and N. Seshagiri, “Federated Predictive Learning with Privacy-Aware Model Aggregation for Distributed Analytics”, IJMLPA, vol. 8, no. 1, pp. 01–15, Jan. 2025, doi: 10.67228/3142788X/IJMLPA-2025PI6L4G.

  • Abstract

    The rapid growth of IoT, edge computing, healthcare, and cyber-physical systems has increased the need for privacy-preserving distributed analytics. Traditional centralized machine learning requires sharing raw data, creating privacy, regulatory, and communication challenges. Federated Learning (FL) enables collaborative model training without exchanging sensitive data but faces limitations such as Non-IID data, communication overhead, privacy leakage, malicious updates, and inefficient aggregation. To address these issues, this study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework. The framework combines secure local training, privacy-aware weighted aggregation, client trust evaluation, and adaptive optimization to improve prediction accuracy, privacy protection, and communication efficiency. It is suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.

  • References

    [1] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.

    [2] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, "Federated optimization in heterogeneous networks," in Proc. MLSys, Austin, TX, USA, 2020.

    [3] J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, "Tackling the objective inconsistency problem in heterogeneous federated optimization," Advances in Neural Information Processing Systems, vol. 33, pp. 7611–7623, 2020.

    [4] S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, "SCAFFOLD: Stochastic controlled averaging for federated learning," in Proc. 37th Int. Conf. Machine Learning (ICML), 2020, pp. 5132–5143.

    [5] Q. 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.

    [6] C. Dwork and A. Roth, "The algorithmic foundations of differential privacy," Foundations and Trends in Theoretical Computer Science, vol. 9, nos. 3–4, pp. 211–407, 2014.

    [7] K. Bonawitz et al., "Practical secure aggregation for privacy-preserving machine learning," in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2017, pp. 1175–1191.

    [8] C. Gentry, A Fully Homomorphic Encryption Scheme. Stanford, CA, USA: Stanford University, 2009.

    [9] N. Papernot et al., "Scalable private learning with PATE," in Proc. Int. Conf. Learning Representations (ICLR), 2018.

    [10] P. Kairouz et al., "Advances and open problems in federated learning," Foundations and Trends in Machine Learning, vol. 14, nos. 1–2, pp. 1–210, 2021.

    [11] P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, "Machine learning with adversaries: Byzantine tolerant gradient descent," Advances in Neural Information Processing Systems, vol. 30, pp. 119–129, 2017.

    [12] D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, "Byzantine-robust distributed learning: Towards optimal statistical rates," in Proc. 35th Int. Conf. Machine

    [13] V. Buterin, "A next-generation smart contract and decentralized application platform," Ethereum White Paper, 2014.

    [14] Y. Liu, J. Kang, D. Niyato, S. Zhang, and C. Miao, "A survey on blockchain for secure federated learning," IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 336–361, 2023.

    [15] B. Li, Y. Wu, J. Song, and X. Wang, "Privacy-preserving federated learning: A survey," IEEE Internet of Things Journal, vol. 10, no. 18, pp. 15983–16009, 2023.

    [16] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [17] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

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