An explainable and trustworthy AI framework for federated learning: a case study in rare autoimmune diseases*

Nowadays, the existence of data silos obscures any advancements in healthcare. On the other hand, conventional centralized data analysis faces numerous obstacles, including legal issues, reduced levels of trustworthiness and lack of data interoperability. Federated artificial intelligence (AI) is an emerging strategy which enables collaborative model training across multiple centers without the need to centralize sensitive patient data. However, the biases which are introduced during the training process combined with the need to install equipment on premises remain underexplored. In this work, we propose a trustworthy, cloud-based AI framework, where federated implementations of high-performance boosting classifiers with hybrid loss functions were developed to solve supervised learning tasks and to provide interpretable risk factors. A case study was conducted to solve an unmet mucosa-associated lymphoid tissue (MALT) lymphoma classification problem by utilizing a PanEuropean data hub with 4805 patients with primary Sjogren’s Syndrome (pSS) (21 European cohorts). Our results highlight the performance of the federated boosting classifiers (0.9 AUC) along with explainable risk factors.Clinical Relevance: The proposed framework was utilized to develop robust MALT lymphoma classifiers for pSS patients by utilizing a PanEuropean data hub with 21 European cohorts across diverse geographical sites yielding favorable classification accuracy along with explainable risk factors for prevention.