论文标题
学习的交易数据：联合学习的励志机制
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning
论文作者
论文摘要
联合学习取决于培训在各种设备上分布的全球模型的概念。在此设置下，用户的设备在自己的数据上执行计算，然后与云服务器共享结果以更新全局模型。此类系统中的一个基本问题是有效地激励用户参与。在联合模型培训过程中，用户遭受其本地数据的隐私泄漏。没有精心设计的激励措施，自私的用户将不愿意参与联合学习任务并贡献其私人数据。为了弥合这一差距，在本文中，我们采用游戏理论来设计有效的激励机制，该机制选择了最有可能提供可靠数据并弥补其隐私泄漏成本的用户。我们将问题提出为两阶段的Stackelberg游戏，并解决了游戏的均衡。广泛的模拟证明了所提出的机制的有效性。
Federated Learning rests on the notion of training a global model distributedly on various devices. Under this setting, users' devices perform computations on their own data and then share the results with the cloud server to update the global model. A fundamental issue in such systems is to effectively incentivize user participation. The users suffer from privacy leakage of their local data during the federated model training process. Without well-designed incentives, self-interested users will be unwilling to participate in federated learning tasks and contribute their private data. To bridge this gap, in this paper, we adopt the game theory to design an effective incentive mechanism, which selects users that are most likely to provide reliable data and compensates for their costs of privacy leakage. We formulate our problem as a two-stage Stackelberg game and solve the game's equilibrium. Effectiveness of the proposed mechanism is demonstrated by extensive simulations.
