Adaptive Asynchronous Split Federated Learning for Medical Image Segmentation

Abstract

Split federated (SplitFed) learning offers promise for collaborative machine learning across decentralized and resource-constrained clients (edge devices, nodes, or organizations) in various applications, including healthcare. However, real-world challenges arise in heterogeneous environments where clients experience communication-related data losses, differ in computational capabilities, and have varying local dataset sizes. In this paper, we introduce adaptive asynchronous split federated learning (AASFL), an innovative training scheme to address such challenges. AASFL blends asynchronous SplitFed learning with client-level adaptability, acknowledging varying client capabilities. In AASFL, each client independently adapts its learning rate and the number of local training epochs based on its local training speed, dataset size, and packet loss probability. To demonstrate the effectiveness of AASFL, we implement and evaluate it on a SplitFed network architecture for medical image segmentation. Moreover, we demonstrate the validity of AASFL by emphasizing the necessity of training each client in the collaborative network and revealing drawbacks of client selection. The results on two public datasets indicate that it greatly enhances global model performance, leading to more accurate segmentation results. We also statistically show that AASFL outperforms the standard SplitFed. Furthermore, we provide a theoretical convergence analysis of AASFL. To the best of our knowledge, this is the first analysis of SplitFed in an adaptive, asynchronous setting. The proposed AASFL training scheme offers a promising avenue for improving medical image segmentation tasks in practical decentralized learning environments.

Keywords

• Adaptive training
• asynchronous training
• medical image segmentation
• split federated learning