Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT

By Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, Yan Zhang. IEEE Transactions on Industrial Informatics, 2020

1,049 citations recorded by the source corpus.

The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/d792ce75ae10d0534cada7fb9c8d6ef316e35a9f

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Citations in the corpus

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, background): “A combination of blockchain and FL is introduced in [37] for a privacy-preserving data exchange in industrial IoT.”
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2019, background): “Lu et al. [16] proposed to leverage blockchain, FL, and DP for data sharing.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “FL fo r Io T D at a Sh ar in g [49] Industrial IoT data sharing CFL NN IoT users Data center A privacy-preserved data sharing scheme for industrial IoT with FL and blockhain.”
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, methodology): “The hospitals can share the dataset during training and information about their dataset is revealed through analyzing the distributed model [29], [30], [31], [32], [33], [34], [35], [36], [37].”
Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles (2020, background): “In [12], the authors designed a blockchain empowered secure data sharing architecture for distributed multiple parties.”
Blockchain for 5G and Beyond Networks: A State of the Art Survey (2019, background): “The work in [195] considers a blockchain empowered secure data sharing architecture for distributed devices in Industrial Internet of Things (IIoT).”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “In particular, blockchain empowered FL was proposed in [310] to provide secure communication and private data sharing schemes for constructing digital twin IIoT networks, followed by reducing communication overheads via asynchronous model aggregation.”
Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System (2019, background): “[9] proposed to leverage blockchain, FL, and differential privacy for data sharing.”
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing (2020, background): “Lu et al. [38] integrated federated learning into the consensus algorithm of the blockchain to save the hash rate.”
Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks (2020, background): “Different from conventional AI algorithms, federated learning based models require transmitting only the parameters from locally trained models to the server while the original data is stored with the user to alleviate security and privacy risks [12].”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, neutral): “Lu et al. [153] propose Blockchain, differential privacy with Federated Learning to solve the issue of data islands for industrial IoT applications.”
Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “In [101], FL was applied to address privacy issues in data sharing among multiple untrusted parties in a blockchain network.”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “FL can help overcome the challenges faced by traditional centralized learning approaches in terms of high privacy concerns since the fact that mobile users may not fully trust third-party servers and thus hesitate to share their private data to MEC servers.”