Federated Learning Architectures for Distributed Robotic Intelligence

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2025PI5X3R

    Published 06-05-2025

  • Federated Learning, Distributed Robotic Intelligence, Edge AI, Multi-Robot Systems, Intelligent Automation, Collaborative Learning, Industry 5.0, Autonomous Robots

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar, “Federated Learning Architectures for Distributed Robotic Intelligence”, IJIARE, vol. 8, no. 1, pp. 01–09, Jun. 2025, doi: 10.67228/30715725/IJIARE-2025PI5X3R.

  • Abstract

    The rapid advancement of intelligent automation and robotics has accelerated the adoption of distributed artificial intelligence across industrial and autonomous systems. Conventional centralized machine learning approaches suffer from privacy risks, communication overhead, and high latency due to cloud-based data processing. Federated Learning (FL) addresses these challenges by enabling multiple robotic agents to collaboratively train shared models while keeping data locally. This paper presents a federated learning framework integrating edge computing, distributed optimization, secure aggregation, and adaptive model synchronization for multi-robot environments. Comparative analysis demonstrates improvements in model accuracy, communication efficiency, privacy preservation, and system robustness over centralized learning. The proposed framework provides a scalable and secure foundation for distributed robotic intelligence, supporting Industry 5.0, smart manufacturing, and cyber-physical production systems.

  • References

    [1] Kairouz, P., McMahan, H. B., Avent, B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210.

    [2] Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.

    [3] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of AISTATS, 1273–1282.

    [4] Bonawitz, K., et al. (2019). Towards federated learning at scale: System design. Proceedings of MLSys, 374–388.

    [5] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.

    [6] Li, Q., Wen, Z., Wu, Z., et al. (2021). A survey on federated learning systems. Journal of Machine Learning Research, 22(184), 1–50.

    [7] Park, J., Samarakoon, S., Bennis, M., & Debbah, M. (2019). Wireless network intelligence at the edge. Proceedings of the IEEE, 107(11), 2204–2239.

    [8] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

    [9] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

    [10] Siciliano, B., & Khatib, O. (2016). Springer Handbook of Robotics (2nd ed.). Springer.

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

    [12] 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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