Open gmontamat opened 5 years ago
Another approach is to: Generate a docker image for the clients (aka workers) code with obfuscated code and encrypted server credentials so that anyone running it cannot modify the code.
public key validation is the way to go. It's a wip for now
The distributed version of the algorithm receives results from the workers without validating their work. This is safe when one has control of all the instances running the worker nodes but it is dangerous when one shares the queue of jobs on the internet. When opening the genetic algorithm to the world, a malicious user may return a fake metric claiming to have trained the model using k-fold cross validation, corrupting the overall result of the parameter tuning. Some options to prevent this from happening are to: