FedARC: Federated Learning for Multi-Center Tuberculosis Chest X-ray Diagnosis with Adaptive Regularizing Contrastive Representation

Tuberculosis (TB) poses a significant global health threat and leads to millions of deaths annually. While early diagnosis and treatment can substantially enhance survival prospects, it continues to present a major challenge, particularly in developing countries. In recent years, machine learning has emerged as a valuable tool for tuberculosis diagnosis. However, the training of a dependable diagnostic model necessitates a large volume of data, typically distributed across multiple medical centers. To safeguard data privacy across various centers, we have incorporated federated learning (FL) into TB diagnosis. However, conventional FL methods suffer from substantial performance degradation due to the considerable variation in TB data distribution across different centers. Consequently, we introduce a novel personalized FL approach, FedARC, to address this issue. To mitigate data heterogeneity across centers, we guide the objective function for each center with adaptive regularization to align it with the stationary point of the global loss, thereby enabling the model to converge towards the global optimum. Simultaneously, model-contrastive learning enables the exploration of the specific attributes of each client, enabling the local model to learn more generalizable features. Extensive experimental results on five publicly available chest X-ray image datasets demonstrate the significant outperformance of our proposed method over state-of-the-art methods in diverse settings.