PedFed: A performance evaluation-driven federated learning framework for efficient communication

Abstract

Protecting healthcare data privacy and security is crucial in advanced manufacturing, which involves medical devices. It encompasses patient records and clinical trial data. Federated learning emerges as a solution that enables model training across different institutions without compromising data privacy and security. However, existing frameworks often exhibit a bias towards clients with larger data volumes, neglecting the connection between global and local model performance. This can result in suboptimal aggregation of the global model, thereby affecting the effectiveness and efficiency of the overall process. To address these limitations, we propose a performance evaluation-driven federated learning framework (PedFed). The primary objective of PedFed is to enhance global model aggregation and improve communication efficiency. Our approach involves a client selection strategy based on performance evaluation of local and global models. Specifically, we introduce the concept of local model improvement (LMI) using Intersection over Union (IoU) for client selection in medical image segmentation scenarios. Moreover, we introduce a dynamic aggregation framework incorporating validation IoU as a weighting factor to mitigate model divergence caused by not independent and identically distributed (non-IID) data. We focus on performing image segmentation tasks to simulate the analysis of sensitive data in the healthcare domain. Experimental results conducted on brain tumor and heart segmentation datasets demonstrate the superiority of the PedFed framework over the baseline framework, confirming its benefits in communication efficiency.