  Federated learning (FL) allows edge devices to collectively learn a model
without directly sharing data within each device, thus preserving privacy and
eliminating the need to store data globally. While there are promising results
under the assumption of independent and identically distributed (iid) local
data, current state-of-the-art algorithms suffer from performance degradation
as the heterogeneity of local data across clients increases. To resolve this
issue, we propose a simple framework, Mean Augmented Federated Learning (MAFL),
where clients send and receive averaged local data, subject to the privacy
requirements of target applications. Under our framework, we propose a new
augmentation algorithm, named FedMix, which is inspired by a phenomenal yet
simple data augmentation method, Mixup, but does not require local raw data to
be directly shared among devices. Our method shows greatly improved performance
in the standard benchmark datasets of FL, under highly non-iid federated
settings, compared to conventional algorithms.
