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Wu, L.; Ruan, W.; Hu, J.; He, Y.; Wu, L. Applications of Blockchain-Based Federated Learning. Encyclopedia. Available online: (accessed on 18 April 2024).
Wu L, Ruan W, Hu J, He Y, Wu L. Applications of Blockchain-Based Federated Learning. Encyclopedia. Available at: Accessed April 18, 2024.
Wu, Lang, Weijian Ruan, Jinhui Hu, Yaobin He, Lang Wu. "Applications of Blockchain-Based Federated Learning" Encyclopedia, (accessed April 18, 2024).
Wu, L., Ruan, W., Hu, J., He, Y., & Wu, L. (2024, March 08). Applications of Blockchain-Based Federated Learning. In Encyclopedia.
Wu, Lang, et al. "Applications of Blockchain-Based Federated Learning." Encyclopedia. Web. 08 March, 2024.
Applications of Blockchain-Based Federated Learning

Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. Blockchain-based federated learning (BFL) has gained the capability and prospects for applications in highly privacy-sensitive industries. 

blockchain federated learning security and privacy Internet of Things

1. Internet of Things

In the realm of IoT, devices are decentralized, and consequently, conducting model training on these devices necessitates both timely and secure data access, as well as robust model generalization capabilities. The research pertaining to the application of BFL within the IoT domain predominantly centers on addressing concerns related to data security, resource allocation, communication protocols, and failure detection [1][2][3][4]. The overarching objective of these efforts is to empower IoT devices to collaboratively train models that exhibit high performance. Lu et al. [5] constructed a distributed multi-party data sharing model that further ensures the authenticity of data through differential privacy, allowing devices to retrieve data securely and accurately. Instead of the common PoW consensus algorithm, the proof of training quality (PoQ) consensus algorithm in [5] is used to verify training models, aiming to improve the utilization efficiency of computational resources. To help household appliance manufacturers improve service quality and optimize appliance functions, Zhao et al. [6] introduced a hierarchical crowdsourcing FL system, utilizing blockchain technology to prevent malicious model updates. To make the 6G network more secure and efficiently apply it to the IoT, Dai et al. [7] proposed a combination of a blockchain and FL, integrating mobile edge computing and device to device (D2D) communication, to address the challenges faced by the 6G network.

2. Industrial Internet of Things

The IIoT encompasses an intricate network of interconnected sensors, equipment, actuators, and other intelligent components. These components facilitate adaptive decision-making and continuous status tracking [8][9], playing a pivotal role in the digital transformation and intelligentization of the contemporary manufacturing industry. In a study conducted by Lu et al. [5], BlockFed was employed to facilitate data sharing within the domain of IIoT. The data-sharing challenge was approached by framing it as an ML problem, incorporating privacy-preserving FL, and integrating FL into the consensus mechanism of a permissioned blockchain. The computational effort required for the consensus was also utilized for federated training. In the context of fault detection scenarios in IIoT, Zhang et al. [10] proposed a federated averaging algorithm called Centroid Distance Weighted Federated Averaging. This algorithm takes into account the distance between negative and positive classes within each client dataset, thereby mitigating the impact of data heterogeneity challenges in IIoT device fault detection. Additionally, Lu et al. [11] recognized the challenges posed by unreliable communication channels, computational resource constraints, and the intricacies associated with establishing trust among users within the context of IIoT. To address these issues, they developed an FL framework for collaborative computation empowered by blockchain technology. This framework substantially elevated the system’s reliability, security, and privacy.

3. Smart Healthcare

BFL can also bring significant advancements to healthcare services. Typically, remote patient monitoring or certain AI-assisted diagnoses require a large amount of patient disease information. However, many medical records contain sensitive information about the patient, and these data have high intrinsic value for certain attackers. As a result, BFL is gradually being applied to the medical field [12][13][14]. Passerat et al. [15] proposed a BFL scheme for healthcare alliances, establishing a set of enterprise-level blockchain components compatible with the Ethereum ecosystem and integrating a series of privacy protection techniques. It also introduced a new secure aggregation protocol designed to run within AMD’s trusted hardware environment, secure encrypted virtualization (SEV), to ensure the security of private data. El Rifai et al. [16] introduced a BFL framework in the medical field, applying smart contracts to the data aggregation process of FL algorithms. This ensures transparency and permission during data sharing, predicting diabetes risk based on training with substantial patient information. Furthermore, Polap et al. [17] developed a lightweight security and privacy algorithm for Internet of Medical Things (IoMT) devices based on BFL. Rahman et al. [18] not only presented a trustworthy BFL framework applicable to the IoMT, but also designed a COVID-19 application for data classification by which we can learn about global models related to COVID-19 diagnoses. This scheme includes a trustworthy and tamper-proof gradient mining method and a decentralized consensus-based aggregator, and adds extra security for blockchain nodes responsible for aggregation. Aich et al. [19] also introduced a BFL scheme for healthcare, aiming to protect and share patients’ medical information by building a real-time global application model. In addition, Kumar et al. [20] proposed a BFL framework that uses the latest data to segment and classify lung CT scans based on capsule networks, sharing data between hospitals to improve COVID-19 detection rates while ensuring patient privacy protection.

4. Internet of Vehicles

BFL solutions have been widely applied to the IoV to facilitate data sharing and autonomous driving [21][22][23]. Pokhrel et al. [21] proposed a fully decentralized BFL framework. This framework achieves end-to-end trustworthy communication within the IoV, and the communication latency remains within an acceptable range, thus promoting effective communication for automated vehicles. They use BFL to verify model updates in on-vehicle machine learning (oVML), enhancing the performance and privacy security of automated vehicles. Lu et al. [24] introduced a BFL framework composed of a primary permissioned blockchain maintained by roadside units and a local DAG run by vehicles, aiming for efficient data sharing in the IoV. Additionally, Lu et al. also proposed an asynchronous FL scheme based on edge data. By using the Delegated Proof of Stake (DPoS), it selects optimized participating nodes, thereby improving the efficiency of FL. In [25], a blockchain-based hierarchical FL algorithm is introduced which reduces storage consumption and improves training accuracy. The proposed knowledge-sharing method based on BFL enhances the reliability and security of in-vehicle networks. Using the proof of learning (PoL) consensus mechanism, a lightweight blockchain was realized, preventing the wastage of computational power.
Additionally, BFL is gradually being expanded to various domains. In the field of content caching, Cui et al. [26] presented a new algorithm called the blockchain-assisted compressed algorithm of FL, applied for content caching (CREAT). This blockchain-assisted FL algorithm aims to predict cache files and enhance the cache hit rate. In the domain of location prediction, the scheme proposed in [27] utilized BFL for local training on users’ mobile devices. This approach safeguards user privacy while making better use of the data for more accurate location predictions. In the realm of mobile crowd sensing, Wang et al. [28] introduced the secure FL for an unmanned aerial vehicle (UAV)-assisted crowdsensing (SFAC) framework. This is a secure FL architecture for UAV-assisted mobile crowd sensing (MCS), employing local differential privacy to protect the privacy of data providers. Moreover, BFL has been applied to disaster response. The study in [29] proposed a blockchain-authorized BFL framework that will implement a disaster response system using wireless mobile modules on UAVs using future 6G networks. Additionally, BFL has also been adopted in the news recommendation field. Wang et al. [30] presented a cloud-edge collaborative filtering recommendation system based on FL. This system incorporates noise into the training model using differential privacy technology, further preventing data privacy exposure.


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