  One of the important problems in federated learning is how to deal with
unbalanced data. This contribution introduces a novel technique designed to
deal with label skewed non-IID data, using adversarial inputs, created by the
I-FGSM method. Adversarial inputs guide the training process and allow the
Weighted Federated Averaging to give more importance to clients with 'selected'
local label distributions. Experimental results, gathered from image
classification tasks, for MNIST and CIFAR-10 datasets, are reported and
analyzed.
