A Poisson Game-Based Incentive Mechanism for Federated Learning in Web 3.0

【Author】 Luo, Mingshun; He, Yunhua; Yuan, Tingli; Wu, Bin; Wu, Yongdong; Xiao, Ke

【Source】IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

【影响因子】5.033

【Abstract】As the next generation of the internet, Web 3.0 is expected to revolutionize the Internet and enable users to have greater control over their data and privacy. Federated learning (FL) enables data to be usable yet invisible during its use, thereby facilitating the transfer of data ownership and value. However, the issues of data size and blockchain computing power are of paramount importance for FL in Web 3.0. Due to the openness of Web 3.0, individuals can freely join or leave training and adjust data size, creating population uncertainty and making it difficult to design incentive mechanisms. Therefore, we propose a Poisson game-based FL incentive mechanism that motivates participants to contribute more data and computing power, considering the variability of data size and computing power requirements, and provides a feasible solution to the uncertainty of the number of participants using a Poisson game model. Additionally, our proposed FL architecture in Web 3.0 integrates FL with Decentralized Autonomous Organizations (DAO), utilizing smart contracts for contribution calculation and revenue distribution. This enables an open, free, and autonomous federated learning environment. Experimental evaluation shows that our incentive mechanism is feasible in blockchain with efficiency, robustness, and low overhead.

【Keywords】Federated learning; incentive mechanism; poisson game; smart contract web 3.0; incentive mechanism; poisson game; smart contract web 3.0

【发表时间】2024 NOV

【收录时间】2024-11-29

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