Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Existing FL works mostly focus on designing advanced FL algorithms to improve the model performance. However, the economic considerations of the clients, such as fairness and incentive, are yet to be fully explored. Without such considerations, self-motivated clients may lose interest and leave the federation. To address this problem, we designed a novel incentive mechanism that involves a client selection process to remove low-quality clients and a money transfer process to ensure a fair reward distribution. Our experimental results strongly demonstrate that the proposed incentive mechanism can effectively improve the duration and fairness of the federation.

翻译：联邦学习（FL）是一种隐私保护的学习技术，能使分布式计算设备跨数据孤岛协作训练共享学习模型。现有联邦学习研究主要聚焦于设计高级联邦学习算法以提升模型性能。然而，客户端的经济考量（如公平性和激励机制）仍未得到充分探索。缺乏此类考量时，自我驱动的客户端可能丧失参与兴趣并退出联合体。为解决该问题，我们设计了一种新型激励机制，包含剔除低质量客户端的客户端选择过程，以及确保公平奖励分配的资金转移过程。实验结果充分表明，所提出的激励机制能有效提升联合体的存续时长与公平性。