Energy-Efficient Distributed Machine Learning in Edge Computing Architectures

  • Authors

    • Dr. Venkatesh Iyer Professor, SRM Institute of Science and Technology, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2025PI4Z1T

    Published 01-03-2025

  • Edge Computing, Distributed Machine Learning, Energy Efficiency, Federated Learning, IoT Systems, Resource Optimization, Green AI, Communication-Efficient Learning

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    V. Iyer, “Energy-Efficient Distributed Machine Learning in Edge Computing Architectures”, IJADSMC, vol. 8, no. 1, pp. 01–16, Jan. 2025, doi: 10.67228/30713498/IJADSMC-2025PI4Z1T.

  • Abstract

    The rapid growth of IoT devices, smart sensors, and real-time intelligent applications has increased the demand for distributed machine learning (DML) in edge computing environments. Traditional cloud-based machine learning approaches often suffer from high latency, excessive bandwidth consumption, privacy concerns, and increased energy costs. Edge computing addresses these challenges by enabling data processing closer to data sources, thereby reducing response time and network congestion. However, deploying machine learning on resource-constrained edge devices introduces challenges related to energy efficiency, computational limitations, communication overhead, and model synchronization. This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization. A distributed learning framework is proposed that integrates local model training, adaptive communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy. Mathematical models are developed to evaluate energy usage, communication costs, and convergence behavior. Experimental results demonstrate significant improvements in energy efficiency, network utilization, convergence speed, and resource management compared with conventional distributed learning approaches. The findings highlight the importance of intelligent communication reduction and adaptive edge orchestration for sustainable AI deployment. The proposed framework supports scalable, privacy-preserving, and real-time analytics in large-scale IoT environments, contributing to the development of next-generation energy-efficient edge intelligence 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 International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.

    [2] J. Konečný, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated Optimization: Distributed Machine Learning for On-Device Intelligence,” arXiv preprint arXiv:1610.02527, 2016.

    [3] 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.

    [4] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated Optimization in Heterogeneous Networks,” in Proceedings of MLSys, 2020, pp. 429–450.

    [5] B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning Differentially Private Recurrent Language Models,” in International Conference on Learning Representations (ICLR), 2018.

    [6] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017.

    [7] X. Ran, H. Chen, X. Zhu, Z. Liu, and J. Chen, “DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics,” in IEEE INFOCOM Workshops, 2018, pp. 1421–1426.

    [8] S. Han, J. Pool, J. Tran, and W. Dally, “Learning Both Weights and Connections for Efficient Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2015, pp. 1135–1143.

    [9] S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding,” in International Conference on Learning Representations (ICLR), 2016.

    [10] G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” arXiv preprint arXiv:1503.02531, 2015.

    [11] Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal Brain Damage,” in Advances in Neural Information Processing Systems, vol. 2, pp. 598–605, 1990.

    [12] J. Dean et al., “Large Scale Distributed Deep Networks,” in Advances in Neural Information Processing Systems, vol. 25, pp. 1223–1231, 2012.

    [13] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.

    [14] K. Bonawitz et al., “Towards Federated Learning at Scale: System Design,” in Proceedings of Machine Learning and Systems (MLSys), 2019, pp. 374–388.

    [15] N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, and F. Kawsar, “An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices,” in Proceedings of the International Workshop on Internet of Things towards Applications (IoT-App), 2015, pp. 7–12.

    [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.

    [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

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

  • Downloads