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Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. However, recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in FL use-cases where the clients’ training involves updating the Batch Normalization (BN) statistics and provide a new baseline attack that works for such scenarios. Furthermore, we present new ways to measure and visualize potential data leakage in FL. Our work is a step towards establishing reproducible methods of measuring data leakage in FL and could help determine the optimal tradeoffs between privacy-preserving techniques, such as differential privacy, and model accuracy based on quantifiable metrics.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Immunosuppression via Janus kinase (JAK) 3 inhibition affords significant prolongation of allograft survival. We investigated the effects of an immunosuppressive regimen combining the JAK3 inhibitor CP-690,550 with mycophenolate mofetil (MMF) in nonhuman primates (NHPs).

Janus Kinase (JAK) 3 is a tyrosine kinase essential for proper signal transduction downstream of selected cytokine receptors and for robust T-cell and natural killer cells activation and function. JAK3 inhibition with CP-690,550 prevents acute allograft rejection. To provide further insight into the mechanisms of efficacy, we investigated the immunomodulatory effects of CP-690,550 in vitro and in vivo in nonhuman primates.

Janus kinase 3 (JAK3) mediates signal transduction from cytokine receptors using the common chain (c). Because mutations in genes encoding c or JAK3 result in immunodeficiency, we investigated the potential of a rationally designed inhibitor of JAK3, CP-690,550, to prevent renal allograft rejection in nonhuman primates.