  Availability of large amount of annotated data is one of the pillars of deep
learning success. Although numerous big datasets have been made available for
research, this is often not the case in real life applications (e.g. companies
are not able to share data due to GDPR or concerns related to intellectual
property rights protection). Federated learning (FL) is a potential solution to
this problem, as it enables training a global model on data scattered across
multiple nodes, without sharing local data itself. However, even FL methods
pose a threat to data privacy, if not handled properly. Therefore, we propose
StatMix, an augmentation approach that uses image statistics, to improve
results of FL scenario(s). StatMix is empirically tested on CIFAR-10 and
CIFAR-100, using two neural network architectures. In all FL experiments,
application of StatMix improves the average accuracy, compared to the baseline
training (with no use of StatMix). Some improvement can also be observed in
non-FL setups.
