  Over the past few years, the field of adversarial attack received numerous
attention from various researchers with the help of successful attack success
rate against well-known deep neural networks that were acknowledged to achieve
high classification ability in various tasks. However, majority of the
experiments were completed under a single model, which we believe it may not be
an ideal case in a real-life situation. In this paper, we introduce a novel
federated adversarial training method for smart home face recognition, named
FLATS, where we observed some interesting findings that may not be easily
noticed in a traditional adversarial attack to federated learning experiments.
By applying different variations to the hyperparameters, we have spotted that
our method can make the global model to be robust given a starving federated
environment. Our code can be found on https://github.com/jcroh0508/FLATS.
