Recently, blockchain-based Federated learning (BCFL) has emerged as a promising technology for promoting data sharing in the Internet of Things (IoT) without relying on a central authority, while ensuring data privacy, security, and traceability. However, it remains challenging to design an decentralized and appropriate incentive scheme that should promise a fair and efficient contribution evaluation for participants while defending against low-quality data attacks. Although Shapley-Value (SV) methods have been widely adopted in FL due to their ability to quantify individuals' contributions, they rely on a central server for calculation and incur high computational costs, making it impractical for decentralized and large-scale BCFL scenarios. In this paper, we designed and evaluated PoShapley-BCFL, a new blockchain-based FL approach to accommodate both contribution evaluation and defense against inferior data attacks. Specifically, we proposed PoShapley, a Shapley-value-enabled blockchain consensus protocol tailored to support a fair and efficient contribution assessment in PoShapley-BCFL. It mimics the Proof-of-Work mechanism that allows all participants to compute contributions in parallel based on an improved lightweight SV approach. Following using the PoShapley protocol, we further designed a fair-robust aggregation rule to improve the robustness of PoShapley-BCFL when facing inferior data attacks. Extensive experimental results validate the accuracy and efficiency of PoShapley in terms of distance and time cost, and also demonstrate the robustness of our designed PoShapley-BCFL. 

PoShapley-BCFL: A Fair and Robust Decentralized Federated Learning Based on Blockchain and the Proof of Shapley-Value

Architectural Patterns for Blockchain-enabled Federated Learning: A Systematic Literature Review

Network data is distributed data on electricity, with the explosive growth of network data, and it has become an inevitable trend of network development to synergized and shared crossdomain scattered data and enhances the value transmission of network data. Federated learning, as a technology that combines data value delivery and data privacy security, is widely concerned in the process of data sharing. However, currently federated learning is used within a single business system. In the process of crossdomain data sharing, how to ensure the data trust, model trust, and result trust of federated learning is still an urgent problem to be solved. To this end, we designed to use blockchain structure to record each behavior of data sharing. Based on its tamper-proof and traceability, combined with cryptography technology, we constructed an endogenous trusted architecture for crossdomain data sharing. In addition, a reverse auction node incentive mechanism based on high credit preference is designed to solve the common problems in data sharing, such as low enthusiasm of users in sharing, unstable data quality of contributions, and unreasonable distribution of data sharing benefits. Through theoretical analysis and experimental verification, it can be seen that the incentive mechanism designed in this paper can meet the authenticity, user rationality, and budget feasibility. On this basis, it can motivate users to participate in data sharing, improve the average quality of data shared by users, and ensure security and trustworthiness and resist malicious attacks to a certain extent.

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Trusted Data Sharing Mechanism Based on Blockchain and Federated Learning in Space-Air-Ground Integrated Networks

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Trusted Data Sharing Mechanism Based on Blockchain and Federated Learning in Space-Air-Ground Integrated Networks