To address the challenges posed by the heterogeneity inherent in federated learning (FL) and to attract high-quality clients, various incentive mechanisms have been employed. However, existing incentive mechanisms are typically utilized in conventional synchronous aggregation, resulting in significant straggler issues. In this study, we propose a novel asynchronous FL framework that integrates an incentive mechanism based on contract theory. Within the incentive mechanism, we strive to maximize the utility of the task publisher by adaptively adjusting clients' local model training epochs, taking into account factors such as time delay and test accuracy. In the asynchronous scheme, considering client quality, we devise aggregation weights and an access control algorithm to facilitate asynchronous aggregation. Through experiments conducted on the MNIST dataset, the simulation results demonstrate that the test accuracy achieved by our framework is 3.12% and 5.84% higher than that achieved by FedAvg and FedProx without any attacks, respectively. The framework exhibits a 1.35% accuracy improvement over the ideal Local SGD under attacks. Furthermore, aiming for the same target accuracy, our framework demands notably less computation time than both FedAvg and FedProx.

翻译：针对联邦学习固有的异构性挑战以及吸引高质量客户的需求，现有研究已采用了多种激励机制。然而，现有激励机制通常应用于传统同步聚合中，导致严重的掉队者问题。本研究提出一种新颖的异步联邦学习框架，该框架融合了基于契约理论的激励机制。在该激励机制中，我们通过自适应调整客户端的本地模型训练轮次，综合考虑时延和测试准确率等因素，力求最大化任务发布者的效用。在异步方案中，我们基于客户端质量设计了聚合权重和访问控制算法以促进异步聚合。通过在MNIST数据集上进行的实验，仿真结果表明：在无攻击情况下，本框架的测试准确率分别比FedAvg和FedProx高出3.12%和5.84%；在遭受攻击时，本框架的准确率比理想情况下的Local SGD提升1.35%。此外，在达到相同目标准确率的前提下，本框架所需的计算时间显著低于FedAvg和FedProx。