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**Fair Multi-party Machine Learning - a game theoretic approach**

**By**\
Chen Zhiliang

Department of Computer Science\
School of Computing\
National University of Singapore\
2019/2020

**Fair Multi-party Machine Learning - a game theoretic approach**

**By**\
Chen Zhiliang

Department of Computer Science\
School of Computing\
National University of Singapore\
2019/2020

Project no.: H148310\
Advisor: Prof Bryan Kian Hsiang Low\
\
Deliverables:\
Report - 1 volume

High performance machine learning models have become highly dependent on the availability of large quantity and quality of training data. To achieve this, various central agencies such as the government have suggested for different data providers to pool their data together to learn a unified predictive model, which performs better. However, these providers are usually profit-driven and would only agree to participate in the data sharing process if the process is deemed both profitable and fair for themselves. Due to the lack of existing literature, it is unclear whether a fair and stable outcome is possible in such data sharing processes. Hence, we wish to investigate the outcomes surrounding these scenarios and study if data providers would even agree to collaborate in the first place. Tapping on cooperative game concepts 