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

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