investigate how an outcome can be optimal. In Section 7, We also analyse various suboptimal outcomes in a data sharing process. Lastly, in Section 8, we perform sensitivity analysis to analyse how sensitive an optimal outcome is towards marginal changes in the data sharing process.

We also perform a series of experiments in Section 9 using two different model valuation measures and demonstrate some results from previous sections.

Related Work
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Literature review reveals that while the study of fairness in the domain of data sharing and collaborative learning has become popular in recent years, most work has focused solely on evaluating the contribution of data providers in a data sharing process or treating the process as non-cooperative. In addition, none of existing works analyses how a fair and stable outcome can be derived after evaluating the data providers’ contribution.\
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The use of *Shapley value* [@9], a classic concept stemming from cooperative game, as an evaluation measure of one’s contribution in data sharing processes [@2; @3] has become increasingly popular in recent years. However, these works focus on alleviating the computational complexities in calculating the *Shapley value* with respect to agents’ data; while the usage of *Shapley value* in 