[ouromoros]

This is also what I was thinking about. Considering that making up bad data does not require any GPU work as opposed to honest calculating nodes, the model can fall quickly if without taking some measures to deal with them (adverserial nodes).

A draft solution would be for the central server to measure the goodness of each update and drop the ones that don't perform well. This could somehow work since inference is much cheaper than gradients computing.