Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation.

Comparison to [3]/[23]: While prior work almost fully characterizes consistency in this class of problems, it is22 quite different from most existing work in statistical learning theory.

We show that diffusion models actually are capable of generating high quality samples, sometimes better than the published results on other types of generative models (Section 4). In addition, we show that a certain parameterization of diffusion models reveals an equivalence with denoising score matching over multiple noise levels during training and with annealed Langevin dynamics during sampling (Section 3.2) [55, 61].