Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning

Understand

For a federated learning model to perform well, it is crucial to have a diverse and representative dataset.

• However, the data contributors may only be concerned with the performance on a specific subset of the population, which may not reflect the diversity of the wider population.
• This creates a tension between the principal (the FL platform designer) who cares about global performance and the agents (the data collectors) who care about local performance.
• In this work, we formulate this tension as a game between the principal and multiple agents, and focus on the linear experiment design problem to formally study their interaction.