Design of a Federated Learning Architecture Supported by Blockchain for Privacy-Preserving Model Training in Internet of Things Health Monitoring Systems
Keywords:
Federated Learning, Blockchain, Internet of Things, Health Monitoring, Privacy, Data Security, Edge ComputingAbstract
The proliferation of Internet of Things (IoT) devices in healthcare systems introduces unprecedented opportunities for continuous health monitoring. However, it also raises significant privacy and security concerns, particularly with the transmission and centralization of sensitive medical data. To address these concerns, this study proposes a novel federated learning (FL) architecture integrated with blockchain to enable decentralized, privacy-preserving model training. The architecture leverages the immutable and auditable nature of blockchain to ensure data integrity and secure model updates across IoT devices. Simulation results demonstrate improved security, data sovereignty, and comparable model performance relative to traditional centralized approaches. This framework provides a scalable and trustworthy solution for modern health monitoring infrastructures.
References
Brisimi, Theodora S., et al. "Federated learning of predictive models from federated electronic health records." International Journal of Medical Informatics, vol. 112, 2018, pp. 59–67.
Xia, Qingyu, et al. "BBDS: Blockchain-based data sharing for electronic medical records in cloud environments." Information, vol. 8, no. 2, 2017, p. 44.
Nguyen, Dinh C., et al. "Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like pandemics: A survey." IEEE Access, vol. 9, 2021, pp. 95730–95753.
Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE Signal Processing Magazine, vol. 37, no. 3, 2020, pp. 50–60.
Sav, Mehmet, et al. "Blockchain-enabled federated learning for secure data sharing in Internet of Vehicles." IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, 2022, pp. 4262–4272.
Yang, Qiang, et al. "Federated machine learning: Concept and applications." ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, 2019, pp. 1–19.
Sharma, Priyanka, et al. "A blockchain-based decentralized federated learning framework for privacy-preserving healthcare." Computer Methods and Programs in Biomedicine, vol. 207, 2021, p. 106198.
Alazab, Mamoun, et al. "IoT security: Review, blockchain solutions, and open challenges." Future Generation Computer Systems, vol. 124, 2021, pp. 169–184.
Rieke, Nicola, et al. "The future of digital health with federated learning." NPJ Digital Medicine, vol. 3, no. 1, 2020, pp. 1–7.
Hussain, Faraz, et al. "A blockchain and federated learning-based framework for smart healthcare monitoring." Sensors, vol. 22, no. 3, 2022, p. 1239.
McMahan, Brendan, et al. "Communication-efficient learning of deep networks from decentralized data." Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282.
Lu, Yujin. "Blockchain and the related issues: A review of current research topics." Journal of Management Analytics, vol. 5, no. 4, 2018, pp. 231–255.
Rahman, Md Masudur, et al. "Privacy-preserving federated learning for wearable health monitoring systems." IEEE Transactions on Industrial Informatics, vol. 18, no. 4, 2022, pp. 2872–2883.
Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends in Machine Learning, vol. 14, no. 1–2, 2021, pp. 1–210.
Zyskind, Guy, Oz Nathan, and Alex Pentland. "Decentralizing privacy: Using blockchain to protect personal data." Proceedings of the 2015 IEEE Security and Privacy Workshops, 2015, pp. 180–184
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Copyright (c) 2021 Anna Kowalska (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
