In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine learning (ML) models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to the required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. To the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that require further research attention.

为了应对各种隐私风险，研究人员和从业者一直在探索不同的范例，这些范例可以利用消费设备增强的计算能力来训练机器学习(ML) 以分布式方式建模，无需将训练数据从单个设备上传到中央设施。为此，联邦学习（FL）被提出作为一种技术，可以通过聚合使用私有数据在本地训练的模型来在中央主节点学习全局机器模型。然而，组织可能不愿意在本地训练模型并共享这些本地机器学习模型，因为最终模型训练所需的计算资源以及对手反转这些模型以推断有关私人训练数据的信息可能导致的隐私风险。人们提出了激励机制来激励最终用户参与机器学习模型的协作训练（使用他们的本地数据），以换取一定的奖励。然而，由于 FL 的分布式特性以及中央服务器无法访问客户端的超参数信息和用于训练的数量/质量数据的事实，FL 的最佳激励机制的设计具有挑战性，这使得确定奖励的任务基于佛罗里达州环境中个人客户的贡献很困难。尽管已经为 FL 提出了几种激励机制，但缺乏彻底的最新系统综述，而本文填补了这一空白。据我们所知，本文是第一篇系统综述，全面列出了实施这些激励机制所需的设计原则，然后根据其设计原则对各种激励机制进行了分类。此外，我们还全面概述了与激励驱动的 FL 相关的安全挑战。最后，我们强调了这些激励计划的局限性和陷阱，并详细阐述了需要进一步研究关注的开放研究问题。