Strategic Analysis of Participants in BCFL-Enabled Decentralized IoT Data Sharing

Abstract

Blockchain-based federated learning (BCFL) has been regarded as an emerging data sharing paradigm in edge networks of internet-of-things (IoT) because of its advantages, such as decentralization, collaborative model training, and privacy protection. However, there have been few studies focusing on strategic analysis in the BCFL system, which is important for establishing a robust and sustainable BCFL system in an untrustworthy and profit-driven environment. In this paper, we first propose a self-organizing data sharing system supported by BCFL to deeply analyze the data sharing logic. Then, a mathematical model based on evolutionary game theory is established to analyze the interaction between model owners and data providers, aiming at exploring the stability of user strategies under different considerations. According to the strategic analysis, we designed and further discussed a dynamic system control mechanism based on smart contracts to adaptively maintain a robust and sustainable BCFL system. We conducted numerical analysis and experiments to verify our work.

Keywords

• blockchain
• data sharing
• internet of things
• evolutionary game
• robust
• stability