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

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-28 DOI:10.1109/TNSE.2024.3450932

Mingshun Luo;Yunhua He;Tingli Yuan;Bin Wu;Yongdong Wu;Ke Xiao

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引用次数: 0

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.

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基于泊松游戏的 Web 3.0 联合学习激励机制

作为下一代互联网，Web 3.0 预计将彻底改变互联网，使用户能够更好地控制自己的数据和隐私。联合学习（Federated Learning，FL）使数据在使用过程中既可使用又不可见，从而促进了数据所有权和价值的转移。然而，数据规模和区块链计算能力问题对于 Web 3.0 中的联合学习至关重要。由于 Web 3.0 的开放性，个人可以自由加入或退出培训，也可以自由调整数据规模，这就造成了群体的不确定性，使激励机制的设计变得困难。因此，我们提出了一种基于泊松博弈的 FL 激励机制，考虑到数据规模和计算能力需求的可变性，激励参与者贡献更多的数据和计算能力，并利用泊松博弈模型为参与者数量的不确定性提供了可行的解决方案。此外，我们在 Web 3.0 中提出的 FL 架构将 FL 与去中心化自治组织（DAO）相结合，利用智能合约进行贡献计算和收入分配。这就实现了一个开放、自由和自主的联合学习环境。实验评估表明，我们的激励机制在区块链中是可行的，而且高效、稳健、开销低。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.