Federated Generative Adversarial Learning
release_mavjckwfy5bl5erorxnryfbqhe
by
Chenyou Fan, Ping Liu
2020
Abstract
This work studies training generative adversarial networks under the
federated learning setting. Generative adversarial networks (GANs) have
achieved advancement in various real-world applications, such as image editing,
style transfer, scene generations, etc. However, like other deep learning
models, GANs are also suffering from data limitation problems in real cases. To
boost the performance of GANs in target tasks, collecting images as many as
possible from different sources becomes not only important but also essential.
For example, to build a robust and accurate bio-metric verification system,
huge amounts of images might be collected from surveillance cameras, and/or
uploaded from cellphones by users accepting agreements. In an ideal case,
utilize all those data uploaded from public and private devices for model
training is straightforward. Unfortunately, in the real scenarios, this is hard
due to a few reasons. At first, some data face the serious concern of leakage,
and therefore it is prohibitive to upload them to a third-party server for
model training; at second, the images collected by different kinds of devices,
probably have distinctive biases due to various factors, e.g.,
collector preferences, geo-location differences, which is also known as "domain
shift". To handle those problems, we propose a novel generative learning scheme
utilizing a federated learning framework. Following the configuration of
federated learning, we conduct model training and aggregation on one center and
a group of clients. Specifically, our method learns the distributed generative
models in clients, while the models trained in each client are fused into one
unified and versatile model in the center. We perform extensive experiments to
compare different federation strategies, and empirically examine the
effectiveness of federation under different levels of parallelism and data
skewness.
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