Federated Learning in Distributed Cloud Systems: Enhancing Privacy and Scalability for Machine Learning in Edge Computing

  • Authors

    • Mr. Kenji Sato Engineering Director, Sony Corporation, Japan. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2020PII0K7Q

    Published 07-03-2020

  • Federated Learning, Distributed Cloud Systems, Edge Computing, Data Privacy, Scalability, Federated Averaging (Fedavg), Hybrid Federated Dual Coordinate Ascent (Hyfdca), Inverse Distance Aggregation, Communication-Efficient Learning, Non-Iid Data

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    K. Sato, “Federated Learning in Distributed Cloud Systems: Enhancing Privacy and Scalability for Machine Learning in Edge Computing”, IJAIDT, vol. 3, no. 2, pp. 01–09, Jul. 2020, doi: 10.67228/30713315/IJAIDT-2020PII0K7Q.

  • Abstract

    In the era of edge computing, where data is generated and processed at the network's edge, ensuring privacy and scalability in machine learning models is paramount. Federated Learning (FL) addresses these challenges by allowing multiple edge devices to collaboratively train models without sharing raw data. This paper investigates the implementation of FL in distributed cloud systems, highlighting its role in preserving data privacy and improving scalability. We analyze various FL algorithms, such as Federated Averaging (FedAvg) and Hybrid Federated Dual Coordinate Ascent (HyFDCA), assessing their effectiveness in edge computing contexts. Additionally, we explore techniques like inverse distance aggregation to handle non-IID data distributions and discuss the trade-offs between communication and computation in FL frameworks. Through comprehensive analysis and experimentation, this study provides insights into optimizing FL for edge computing, paving the way for more secure and scalable machine learning applications in distributed cloud environments.

  • References

    [1] Federated Learning in Mobile Edge Networks: A Comprehensive Survey — Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Qiang, Y., Niyato, D., & Miao, C. (2019). arXiv preprint. arXiv

    [2] Federated Learning: A Privacy-Preserving Approach for Distributed Machine Learning — (IJERT). This work reviews the architecture and implementation of FL in privacy-sensitive environments, especially for IoT and distributed data contexts. IJERT

    [3] Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2018). Adaptive Federated Learning in Resource Constrained Edge Computing Systems. arXiv:1804.05271.

    [4] Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated Learning with Non-IID Data. arXiv:1806.00582.

    [5] Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2018). Federated Learning via Over-the-Air Computation. arXiv:1812.11750.

    [6] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017/2018 cited). Communication-Efficient Learning of Deep Networks from Decentralized Data.

    [7] Bonawitz, K., Eichner, H., Grieskamp, W., et al. (2018). Towards Federated Learning at Scale: System Design.

    [8] Konečný, J., McMahan, H. B., Yu, F. X., et al. (2016/2018 cited). Federated Learning: Strategies for Improving Communication Efficiency.

    [9] Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2018). Federated Learning: Challenges, Methods, and Future Directions.

    [10] Nishio, T., & Yonetani, R. (2018). Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge.

    [11] Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2018). Federated Multi-Task Learning.

    [12] Geyer, R. C., Klein, T., & Nabi, M. (2018). Differentially Private Federated Learning: A Client Level Perspective.

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