Trade-off between Payoff and Model Rewards in Fair Collaborative Machine Learning

Nov 28, 2022

Speakers

About

This paper investigates the problem of fairly trading off between payoff and model rewards in collaborative machine learning (ML) where parties aggregate their datasets together to obtain improved ML models over that of each party. Supposing parties can afford the optimal model trained on the aggregated dataset, we propose an allocation scheme that distributes the payoff fairly. Notably, the same scheme can be derived from two different approaches based on (1) desirable properties of the parties' payoffs or (2) that of the underlying payoff flows from one party to another. While the former is conceptually simpler, the latter can be used to handle the practical constraint on the budgets of parties. In particular, we propose desirable properties for achieving a fair adjustment of the payoff flows that can trade off between the model reward's performance and the payoff reward. We empirically demonstrate that our proposed scheme is a sensible solution in several scenarios of collaborative ML with different budget constraints.

Organizer

Like the format? Trust SlidesLive to capture your next event!

Professional recording and live streaming, delivered globally.

Sharing

Recommended Videos

Presentations on similar topic, category or speaker

Interested in talks like this? Follow NeurIPS 2022