Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.
翻译:联邦学习联盟利用参与者提供的各种资源,合作培训全球模式,这有可能解决机器学习的数据隐私问题,在这种有希望的模式中,如果没有足够的培训数据和学习过程中的其他资源,业绩将恶化,因此,激励更多的参与者贡献宝贵资源,为联合会学习支付一些费用,这是相当关键的。在本文件中,我们对联合会学习的奖励计划进行了全面调查。具体地说,我们确定了联合会学习的激励问题,然后为各种计划提供了分类。随后,我们从主要技术方面总结了现有的奖励机制,例如斯塔克尔伯格游戏、拍卖、合同理论、夏普利价值、强化学习、障碍链。通过审查和比较一些令人印象深刻的成果,我们为未来的研究找出了三个方向。