Federated Analytics for Privacy-Preserving Edge Computing

  • Authors

    • Dr. John McCarthy Professor of Computer Science, Stanford University, United States. Author
    • Dr. Marvin Minsky Professor, MIT, United States. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2022PI8X5C

    Published 05-14-2022

  • Federated Analytics, Edge Computing, Privacy-Preserving, Differential Privacy, Secure Multiparty Computation, Homomorphic Encryption, Internet Of Things, Distributed Systems, Data Security

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    J. McCarthy and M. Minsky, “Federated Analytics for Privacy-Preserving Edge Computing”, IJDEIC, vol. 5, no. 1, pp. 01–13, May 2022, doi: 10.67228/30715717/IJDEIC-2022PI8X5C.

  • Abstract

    With the rapid expansion of edge computing, vast volumes of sensitive data are now being generated and processed at the network's periphery, raising significant concerns about privacy and data security. Federated Analytics (FA) emerges as a transformative solution by enabling decentralized data analysis without the need to transfer raw data to central servers, thereby mitigating potential privacy breaches. This study investigates the integration of FA into edge computing ecosystems, leveraging advanced Privacy-Enhancing Technologies (PETs) such as Differential Privacy (DP), Secure Multiparty Computation (SMC), and Homomorphic Encryption (HE) to ensure robust privacy protections. A multi-layered architecture is proposed and evaluated using simulations on Raspberry Pi clusters and synthetic workload datasets to emulate real-world edge environments. Experimental results indicate that FA, especially when combined with DP, achieves a strong balance between analytical accuracy and computational efficiency, while SMC and HE offer enhanced security at the cost of increased computational overhead. The findings underscore the practicality and effectiveness of FA for privacy-preserving analytics at the edge, suggesting its potential to support compliance with data protection regulations and meet the demands of future applications. The paper concludes by emphasizing the need for further research in optimizing scalability, minimizing resource usage, and exploring synergies with emerging technologies such as 6G and intelligent orchestration platforms to fully realize the promise of federated edge analytics.

  • References

    [1] Kairouz, P., McMahan, H. B., et al. (2021). Advances and Open Problems in Federated Learning. Found. Trends in Machine Learning.

    [2] Bonawitz, K., et al. (2022). Practical Secure Aggregation. ACM CCS.

    [3] Gentry, C. (2009). Fully Homomorphic Encryption. Stanford University.

    [4] Dwork, C. (2006). Differential Privacy. ICALP.

    [5] EdgeCloudSim: https://github.com/CagataySonmez/EdgeCloudSim

    [6] 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.

    [7] 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.

    [8] K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, "Practical Secure Aggregation for Privacy-Preserving Machine Learning," in Proc. ACM SIGSAC Conference on Computer and Communications Security (CCS), 2017, pp. 1175–1191.

    [9] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, May 2020.

    [10] 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.

  • Downloads