In recent years, there has been a significant increase in attention towards designing incentive mechanisms for federated learning (FL). Tremendous existing studies attempt to design the solutions using various approaches (e.g., game theory, reinforcement learning) under different settings. Yet the design of incentive mechanism could be significantly biased in that clients' performance in many applications is stochastic and hard to estimate. Properly handling this stochasticity motivates this research, as it is not well addressed in pioneering literature. In this paper, we focus on cross-device FL and propose a multi-level FL architecture under the real scenarios. Considering the two properties of clients' situations: uncertainty, correlation, we propose FL Incentive Mechanism based on Portfolio theory (FL-IMP). As far as we are aware, this is the pioneering application of portfolio theory to incentive mechanism design aimed at resolving FL resource allocation problem. In order to more accurately reflect practical FL scenarios, we introduce the Federated Learning Agent-Based Model (FL-ABM) as a means of simulating autonomous clients. FL-ABM enables us to gain a deeper understanding of the factors that influence the system's outcomes. Experimental evaluations of our approach have extensively validated its effectiveness and superior performance in comparison to the benchmark methods.

翻译：近年来，针对联邦学习激励机制设计的研究日益受到关注。大量现有研究尝试在不同场景下采用多种方法（例如博弈论、强化学习）设计解决方案。然而，由于许多应用场景中客户端性能具有随机性且难以估计，激励机制的设计可能产生显著偏差。妥善处理这种随机性推动了本研究，因为现有文献尚未充分解决这一问题。本文聚焦跨设备联邦学习，提出一种面向真实场景的多层联邦学习架构。针对客户端状况的两个特性（不确定性与相关性），我们提出了基于组合理论的联邦学习激励机制（FL-IMP）。据我们所知，这是将组合理论应用于激励机制设计以解决联邦学习资源分配问题的开创性工作。为更准确反映实际联邦学习场景，我们引入基于联邦学习智能体的模型（FL-ABM）来模拟自主客户端。FL-ABM使我们能够深入理解影响系统结果的因素。实验评估充分验证了本方法相较于基准方法的有效性和优越性能。