Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

By Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusist Niyato, Zengxiang Li, Lingjuan Lyu, Yingbo Liu. IEEE Internet of Things Journal, 2019

508 citations recorded by the source corpus.

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers’ data. Then, manufacturers can predict customers’ requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile-edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers’ activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers’ privacy and improve the test accuracy, we enforce differential privacy (DP) on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under DP protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f25811db6f2de1fe01705d91234ca0bf84e47666

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Citations in the corpus

Local Differential Privacy-Based Federated Learning for Internet of Things (2020, background): “Furthermore, there have been many papers on FL and differential privacy, such as [31], [35]–[43].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “A number of possible research directions are also highlighted.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “Blockchain [37] can be used to address this issue, aiming to replace the centralized aggregator in the traditional FL system [103].”
Federated Learning for Smart Healthcare: A Survey (2021, background): “This would reduce the risks of the leakage of sensitive user information to the external third-party, and thus providing a higher degree of user privacy [30].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “The authors of [207] proposed a data-protecting blockchain-based FL for IoT devices.”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, methodology): “To further improve the data privacy against the attacks inverting model parameters into raw data [41], [57], various privacy-preserving methods have been investigated, such as injecting fine-tuned noise into model parameters via a differential privacy mechanism [31], [58]–[60] and mixing model param”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “different decentralized designs: P2P [95], [200], graph [118] or blockchain [176], [202].”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “To extend reputationbased employee selection in [320], the authors propose a reputation-based crowdsourcing incentive mechanism in [321]”