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，联邦学习（FL）激励机制的设计受到广泛关注。大量现有研究试图在不同场景下采用多种方法（如博弈论、强化学习）设计方案。然而，由于许多应用中客户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。恰当地处理这种随机性正是本研究的动机，而这一课题在早期文献中尚未得到充分解决。本文聚焦于跨设备联邦学习，提出了一种面向真实场景的多层级FL架构。考虑到客户情境的两个特性——不确定性与相关性，我们提出了基于投资组合理论的FL激励机制（FL-IMP）。据我们所知，这是投资组合理论首次被应用于激励机制设计以解决FL资源分配问题。为更准确地反映实际FL场景，我们引入联邦学习智能体基模型（FL-ABM）来模拟自主客户。FL-ABM使我们能够深入理解影响系统结果的因素。通过实验评估，我们的方法在有效性及性能上均显著优于基准方法，并得到广泛验证。