Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

John Owoicho Odeh; Xiaolong Yang; Cosmas Ifeanyi Nwakanma; Sahraoui Dhelim

doi:10.3390/electronics13091610

journal article Open Access Apr 23, 2024

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

John Owoicho Odeh

Xiaolong Yang

Cosmas Ifeanyi Nwakanma

Sahraoui Dhelim

Electronics Vol. 13 No. 9 pp. 1610 · MDPI AG

View at Publisher Save 10.3390/electronics13091610

Abstract

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

Topics

No keywords indexed for this article. Browse by subject →

References

33

[1]

Shao "Tracing the evolution of AI in the past decade and forecasting the emerging trends" Expert Syst. Appl. (2022) 10.1016/j.eswa.2022.118221

[2]

Mohjazi "Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges" IEEE Trans. Emerg. Top. Comput. Intell. (2023) 10.1109/tetci.2023.3251404

[3]

Vailshery, L.S. (2023, July 27). Number of Internet of Things (IoT) Connected Devices Worldwide from 2019 to 2023, with Forecasts from 2022 to 2030, Available online: http://xxx.lanl.gov/abs/https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/.

[4]

Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V. (2019, January 19–23). Exploiting Unintended Feature Leakage in Collaborative Learning. Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA. 10.1109/sp.2019.00029

[5]

Wang "Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing" IEEE J. Biomed. Health Inform. (2023) 10.1109/jbhi.2022.3157725

[6]

Ksentini "On Extending ETSI MEC to Support LoRa for Efficient IoT Application Deployment at the Edge" IEEE Commun. Stand. Mag. (2020) 10.1109/mcomstd.001.1900051

[7]

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Xiaohong Huang, Yueyue Dai, Sabita Maharjan

IEEE Transactions on Industrial Informatics 2020 10.1109/tii.2019.2942179

[8]

Yang "Federated Learning for 6G: Applications, Challenges, and Opportunities" Engineering (2022) 10.1016/j.eng.2021.12.002

[9]

Privacy-Preserving Asynchronous Federated Learning Framework in Distributed IoT

Xinru Yan, Yinbin Miao, Xinghua Li et al.

IEEE Internet of Things Journal 2023 10.1109/jiot.2023.3262546

[10]

Qolomany, B., Ahmad, K., Al-Fuqaha, A., and Qadir, J. (2020, January 7–11). Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services. Proceedings of the GLOBECOM 2020–2020 IEEE Global Communications Conference, Taipei, Taiwan. 10.1109/globecom42002.2020.9322464

[11]

Micro-LED as a Promising Candidate for High-Speed Visible Light Communication

Konthoujam James Singh, Yu-Ming Huang, Tanveer Ahmed et al.

Applied Sciences 10.3390/app10207384

[12]

Visible Light Communication in 6G: Advances, Challenges, and Prospects

Nan Chi, Yingjun Zhou, Yiran Wei et al.

IEEE Vehicular Technology Magazine 2020 10.1109/mvt.2020.3017153

[13]

McMahan, H.B., Ramage, D., Talwar, K., and Zhang, L. (2018). Learning Differentially Private Recurrent Language Models. arXiv.

[14]

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., and Seth, K. (November, January 30). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA. 10.1145/3133956.3133982

[15]

Zhao "Secure Multi-Party Computation: Theory, practice and applications" Inf. Sci. (2019) 10.1016/j.ins.2018.10.024

[16]

Sun "Update or Wait: How to Keep Your Data Fresh" IEEE Trans. Inf. Theory (2017) 10.1109/tit.2017.2735804

[17]

Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., and Liu, Y. (2020, January 15–17). {BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning. Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 20), Online.

[18]

Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

Yuntao Wang, Zhou Su, Ning Zhang et al.

IEEE Transactions on Network Science and Engineeri... 2021 10.1109/tnse.2020.3014385

[19]

Yu, Z., Hu, J., Min, G., Lu, H., Zhao, Z., Wang, H., and Georgalas, N. (2018, January 9–13). Federated Learning Based Proactive Content Caching in Edge Computing. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates. 10.1109/glocom.2018.8647616

[20]

Giovanelli "Task Allocation Algorithm for Energy Resources Providing Frequency Containment Reserves" IEEE Trans. Ind. Inform. (2019) 10.1109/tii.2018.2821676

[21]

Coutinho "Modeling and Analysis of a Shared Edge Caching System for Connected Cars and Industrial IoT-Based Applications" IEEE Trans. Ind. Inform. (2020) 10.1109/tii.2019.2938529

[22]

Xie, C., Koyejo, S., and Gupta, I. (2020). Asynchronous Federated Optimization. arXiv.

[23]

Wang, Z., Zhang, Z., and Wang, J. (2021, January 14–23). Asynchronous Federated Learning over Wireless Communication Networks. Proceedings of the ICC 2021—IEEE International Conference on Communications, Montreal, QC, Canada. 10.1109/icc42927.2021.9500860

[24]

Nowak "Verticals in 5G MEC-Use Cases and Security Challenges" IEEE Access (2021) 10.1109/access.2021.3088374

[25]

Froehlich, A., and Ferguson, K. (2024, April 15). Bandwidth (network bandwidth), TechTarget, Available online: http://xxx.lanl.gov/abs/https://www.techtarget.com/searchnetworking/definition/bandwidth.

[26]

Mengistu, T.M., Kim, T., and Lin, J.W. (2024). A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning. Sensors, 24. 10.3390/s24030968

[27]

Mahbub "Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions" J. Netw. Comput. Appl. (2023) 10.1016/j.jnca.2023.103726

[28]

Mudassar "Adaptive Fault-Tolerant Strategy for Latency-Aware IoT Application Executing in Edge Computing Environment" IEEE Internet Things J. (2022) 10.1109/jiot.2022.3144026

[29]

Thantharate, P., and Anurag, T. (2023, January 4–6). CYBRIA—Pioneering Federated Learning for Privacy-Aware Cybersecurity with Brilliance. Proceedings of the 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET), Boca Raton, FL, USA. 10.1109/honet59747.2023.10374608

[30]

Sonmez, C., Ozgovde, A., and Ersoy, C. (2017, January 8–11). EdgeCloudSim: An environment for performance evaluation of Edge Computing systems. Proceedings of the 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), Valencia, Spain. 10.1109/fmec.2017.7946405

[31]

LeCun, Y., Cortes, C., and Burges, C. (2010). MNIST Handwritten Digit Database, ATT Labs. Available online: http://yann.lecun.com/exdb/mnist.

[32]

Jiang, C., Li, Y., Su, J., and Chen, Q. (2021). Research on new edge computing network architecture and task offloading strategy for Internet of Things. Wirel. Netw., 1–13. 10.1007/s11276-020-02516-8

[33]

Zhao "Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training" IEEE Trans. Image Process. (2022) 10.1109/tip.2022.3201602

Metrics

6

Citations

33

References

Details

Published: Apr 23, 2024
Vol/Issue: 13(9)
Pages: 1610
License: View

Authors

J

John Owoicho Odeh

Computer and Communication Engineering, University of Science and Technology, Beijing 100083, China

X

Xiaolong Yang

Computer and Communication Engineering, University of Science and Technology, Beijing 100083, China

C

Cosmas Ifeanyi Nwakanma

ICT-Convergence Research Center, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea

S

Sahraoui Dhelim

School of Computer Science, University College Dublin, Belfield, D04V1w8 Dublin, Ireland

Funding

National Natural Science Foundation of China (NSFC) Award: 61971033

Cite This Article

John Owoicho Odeh, Xiaolong Yang, Cosmas Ifeanyi Nwakanma, et al. (2024). Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem. Electronics, 13(9), 1610. https://doi.org/10.3390/electronics13091610

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

You May Also Like