Foundation models matter: federated learning for multi-center tuberculosis diagnosis via adaptive regularization and model-contrastive learning

Abstract

In tackling Tuberculosis (TB), a critical global health challenge, the integration of Foundation Models (FMs) into diagnostic processes represents a significant advance. FMs, with their extensive pre-training on diverse datasets, hold the promise of transforming TB diagnosis by leveraging their deep understanding and analytical capabilities. However, the application of these models in healthcare is complicated by the need to protect patient privacy, particularly when dealing with sensitive TB data from various medical centers. Our novel approach, FedARC, addresses this issue through personalized federated learning (PFL), enabling the use of private data without direct access. FedARC innovatively navigates data heterogeneity and privacy concerns by employing adaptive regularization and model-contrastive learning. This method not only aligns each center’s objective function with the global loss’s stationary point but also enhances model generalization across disparate data sources. Comprehensive evaluations on five publicly available chest X-ray image datasets demonstrate that foundation models profoundly influence outcomes, with our proposed method significantly surpassing contemporary methodologies in various scenarios.