  Artificial intelligence (AI) provides a promising substitution for
streamlining COVID-19 diagnoses. However, concerns surrounding security and
trustworthiness impede the collection of large-scale representative medical
data, posing a considerable challenge for training a well-generalised model in
clinical practices. To address this, we launch the Unified CT-COVID AI
Diagnostic Initiative (UCADI), where the AI model can be distributedly trained
and independently executed at each host institution under a federated learning
framework (FL) without data sharing. Here we show that our FL model
outperformed all the local models by a large yield (test sensitivity
/specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving
comparable performance with a panel of professional radiologists. We further
evaluated the model on the hold-out (collected from another two hospitals
leaving out the FL) and heterogeneous (acquired with contrast materials) data,
provided visual explanations for decisions made by the model, and analysed the
trade-offs between the model performance and the communication costs in the
federated training process. Our study is based on 9,573 chest computed
tomography scans (CTs) from 3,336 patients collected from 23 hospitals located
in China and the UK. Collectively, our work advanced the prospects of utilising
federated learning for privacy-preserving AI in digital health.
