Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners’ raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for incentivizing data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.

中文翻译：

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联邦学习的激励机制：从经济学和博弈论的角度


联邦学习（FL）变得流行，并在不暴露所有者原始数据的情况下训练大规模机器学习（ML）模型方面显示出巨大潜力。在 FL 中，数据所有者可以根据本地数据训练 ML 模型，并且仅将模型更新而不是原始数据发送给模型所有者进行聚合。为了提高模型准确性和训练完成时间方面的学习绩效，招募足够的参与者至关重要。同时，数据拥有者是理性的，可能会因为资源消耗而不愿意参与协作学习过程。为了解决这些问题，最近提出了各种工作来激励数据所有者贡献其资源。在本文中，我们对文献中提出的经济和博弈论方法进行了全面的回顾，以设计各种方案来激励数据所有者参与 FL 训练过程。特别是，我们首先介绍了 FL 的基本原理和背景，即激励机制设计中常用的经济理论。然后，我们回顾了博弈论和经济方法在 FL 激励机制设计中的应用。最后，我们强调了关于 FL 激励机制设计的一些悬而未决的问题和未来的研究方向。