联邦学习中员工的激励：Stackelberg博弈视角
Motivating Workers in Federated Learning: A Stackelberg Game Perspective
摘要：
由于训练数据的大规模，分布式学习方法，如联邦学习最近得到了关注。然而，分布式学习的收敛速度受到异构工人的性能。在本文中，我们考虑了一种激励机制，工人，以减轻延迟完成每一批。我们解析地得到了一个Stackelberg对策的均衡解。我们的数值结果表明，在预算有限的情况下，模型所有者应该明智地决定工人的数量，因为工人数量提供的多样性和完成训练的延迟之间存在权衡。
英文摘要：
Due to the large size of the training data, distributed learning approaches such as federated learning have gained attention recently. However, the convergence rate of distributed learning suffers from heterogeneous worker performance. In this paper, we consider an incentive mechanism for workers to mitigate the delays in completion of each batch. We analytically obtained equilibrium solution of a Stackelberg game. Our numerical results indicate that with a limited budget, the model owner should judiciously decide on the number of workers due to trade off between the diversity provided by the number of workers and the latency of completing the training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
