Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory

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 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, our framework achieved a test accuracy that is 3.12% and 5.84% higher than the accuracy achieved by FedAvg and FedProx without any attacks, respectively. Under attacks, the framework exhibits a 1.35% accuracy improvement over the ideal Local SGD. Furthermore, aiming for the same target accuracy, our framework demands notably less computation time than both FedAvg and FedProx.