Abstract: Embodiments of the invention are generally directed to methods and systems for robust federated training of neural networks capable of overcoming sample size and/or label distribution heterogeneity. In various embodiments, a neural network is trained by performing a first number of training iterations using a first set of training data and performing a second number of training iterations using a second set of training data, where training methodology includes a function to compensate for at least one form of heterogeneity. Certain embodiments incorporate image generation networks to produce synthetic images used to train a neural network.