collaborative process.

![Overview of data sharing process and this paper’s focus](intro.png)

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The first challenge arises in terms of evaluating the contribution of each agent in a multi-party machine learning process - when data is jointly pooled from multiple sources to create a unified model, what is the relative contribution of each agent in the collaborative effort? Answering this question is paramount because the reward each agent walks away from collaboration heavily depends on his relative contribution.

The next challenge arises after one has established the relative contribution of each agent in the collaborative process - given this contribution measure, how can we reward the participating agents appropriately after collaboration (the concept of ’reward’ here refers to the value of the resulting model that each agent receives after collaborating)? While the reward given to an agent should clearly commensurate his contribution (fair), it should also be attractive enough to ensure agents are incentivised to continue collaborating (stable). We need to investigate if such fair and stable allocation of reward is even possible given the data contributed by each agent, and if not, what possible alternatives are available. To contextualise the above challenges, we introduce the following motivating example.

A motivating 