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.

我们提出了一种新颖的异步联邦学习框架，该框架整合了基于合同理论的激励机制，通过自适应地调整客户端的本地模型训练轮数来最大化任务发布者的效用，考虑到时间延迟和测试准确性等因素。通过在MNIST数据集上进行的实验，模拟结果表明，我们的框架比没有任何攻击的FedAvg和FedProx分别提高了3.12％和5.84％的测试准确性。在受到攻击时，该框架比理想的本地SGD准确性提高了1.35％。此外，针对相同的目标准确性，我们的框架所需的计算时间明显少于FedAvg和FedProx。

基于合同理论的异步联邦学习与激励机制