example behind fair multi-party machine learning
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Artificial intelligence has shown great promise in healthcare for many applications such as tumour detection and the diagnosis of eye diseases. However, much of its performance is dependent on the availability of sufficient high quality data.

![BioMind, developed by AI Research Centre for Neurological Disorders and Capital Medical University, beat human experts in a brain tumour detection competition in June 2018](biomind.png)

Assume a government healthcare agency wants hospitals to use patients’ MRI [^1] scans to create a model for automatic tumour detection. However, data held by individual hospital may be insufficient to produce a good model. From the point of view of the government, it is desirable for all the hospitals to pool their data to create a unified model - this implies that patients enjoy better predictive results. However in reality, hospitals are profit-driven and will only agree to participate if the following two criteria are met: first, a hospital wants the reward gained from the collaborative effort to be higher than any possible reward derived when it chooses to work alone or with a smaller group of hospitals. Second, a hospital expects the reward received to be fair and proportional to 