machine learning. As massive amount of data is held by different data providers, it becomes worthwhile for them to work together, possibly combining these datasets to learn a unified predictive model. Each data provider can then ideally make use of this model, which arguably performs better than any model created from small individual dataset. In fact, the importance of pooling data together from multiple sources has been underscored by various public initiatives such as the *Ocean Protocol* framework (created jointly by Pricewaterhousecoopers Singapore and Singapore-based startup DEX). From the point of view of a central agency, such as the government, data sharing is desirable because citizens, businesses and society stand to benefit from better predictive models in general.

Most work on collaborative machine learning in a multi-agent setting then focuses on parallelizing the process of learning a predictive model from decentralized data sources. However, there has been little to no work focused on evaluating the relative contribution of agents and investigating the outcomes surrounding such collaborative processes. In particular, we wish to investigate if we can measure each agent’s relative contribution reasonably and find a sufficiently fair reward to allocate to each player so that they are satisfied with the 