Good questions. Hmm do you think maybe it would be viable if the payouts were based on [model] performance rather than ostensible training time? There's a usef

Good questions. Hmm do you think maybe it would be viable if the payouts were based on [model] performance rather than ostensible training time? There's a useful asymmetry we can exploit: finding weights that perform well is computationally intensive and takes time, but scoring a set of weights is fast and easy. A number cruncher could spend 2 weeks training a model, and then when they submit the results it takes me 10 seconds to score the model - to verify the quality of the results, and calculate the performance-based payout. In the #1 or #3 scenario where they didn't do or didn't complete the computations, they wouldn't have a well-trained model to submit for payout. (The lost time in #3 is inconvenient in time-sensitive situations, but mechanisms exist to address that - SLAs, up-front collateral, etc) Regarding privacy, that's an EXTREMELY good and important question. There's some really neat prior art for privacy-preserving machine learning that could be useful here, e.g. https://arxiv.org/abs/2106.07229 https://arxiv.org/abs/2106.07229 "Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network" (note I'm approaching this as an interesting DistML thought experiment, not proposing it as an immediately viable or sensible initiative)