Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Authors

  • Sebastian Shenghong Tay National University of Singapore Institute for Infocomm Research, A*STAR
  • Xinyi Xu National University of Singapore Institute for Infocomm Research, A*STAR
  • Chuan Sheng Foo Institute for Infocomm Research, A*STAR
  • Bryan Kian Hsiang Low National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v36i9.21177

Keywords:

Multiagent Systems (MAS), Game Theory And Economic Paradigms (GTEP), Machine Learning (ML)

Abstract

This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e.g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions. Distributing synthetic data as rewards (instead of trained models or money) offers task- and model-agnostic benefits for downstream learning tasks and is less likely to violate data privacy regulation. To realize the framework, we firstly propose a data valuation function using maximum mean discrepancy (MMD) that values data based on its quantity and quality in terms of its closeness to the true data distribution and provide theoretical results guiding the kernel choice in our MMD-based data valuation function. Then, we formulate the reward scheme as a linear optimization problem that when solved, guarantees certain incentives such as fairness in the CGM framework. We devise a weighted sampling algorithm for generating synthetic data to be distributed to each party as reward such that the value of its data and the synthetic data combined matches its assigned reward value by the reward scheme. We empirically show using simulated and real-world datasets that the parties' synthetic data rewards are commensurate to their contributions.

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Published

2022-06-28

How to Cite

Tay, S. S., Xu, X., Foo, C. S., & Low, B. K. H. (2022). Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9448-9456. https://doi.org/10.1609/aaai.v36i9.21177

Issue

Section

AAAI Technical Track on Multiagent Systems