  Deep Learning-based image synthesis techniques have been applied in
healthcare research for generating medical images to support open research and
augment medical datasets. Training generative adversarial neural networks
(GANs) usually require large amounts of training data. Federated learning (FL)
provides a way of training a central model using distributed data while keeping
raw data locally. However, given that the FL server cannot access the raw data,
it is vulnerable to backdoor attacks, an adversarial by poisoning training
data. Most backdoor attack strategies focus on classification models and
centralized domains. It is still an open question if the existing backdoor
attacks can affect GAN training and, if so, how to defend against the attack in
the FL setting. In this work, we investigate the overlooked issue of backdoor
attacks in federated GANs (FedGANs). The success of this attack is subsequently
determined to be the result of some local discriminators overfitting the
poisoned data and corrupting the local GAN equilibrium, which then further
contaminates other clients when averaging the generator's parameters and yields
high generator loss. Therefore, we proposed FedDetect, an efficient and
effective way of defending against the backdoor attack in the FL setting, which
allows the server to detect the client's adversarial behavior based on their
losses and block the malicious clients. Our extensive experiments on two
medical datasets with different modalities demonstrate the backdoor attack on
FedGANs can result in synthetic images with low fidelity. After detecting and
suppressing the detected malicious clients using the proposed defense strategy,
we show that FedGANs can synthesize high-quality medical datasets (with labels)
for data augmentation to improve classification models' performance.
