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.

本文提出了一种新的协作生成建模 (CGMO) 框架，通过使用最大均值差 (MMD) 数据估价函数和线性优化问题作为奖励计划，来激励个体之间的协作，将合成数据作为奖励分配给这些个体，同时保证合理的激励机制。