[2101.07235] Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary

We introduce FELICIA (FEderated LearnIng with a CentralIzed Adversary) a generative mechanism enabling collaborative learning. In particular, we show how a data owner with limited and biased data could benefit from other data owners while keeping data from all the sources private. This is a common scenario in medical image analysis where privacy legislation prevents data from being shared outside local premises. FELICIA works for a large family of Generative Adversarial Networks (GAN) architectures including vanilla and conditional GANs as demonstrated in this work. We show that by using the FELICIA mechanism, a data owner with limited image samples can generate high-quality synthetic images with high utility while neither data owners has to provide access to its data. The sharing happens solely through a central discriminator that has access limited to synthetic data. Here, utility is defined as classification performance on a real test set. We demonstrate these benefits on several re

2 mentions: @AkiraTOSEI@AkiraTOSEI
Keywords: 医療
Date:

Referring Tweets

@AkiraTOSEI
@AkiraTOSEI t.co/HflfUsx2he Federated Learningの枠組みで各関係者に分散しているデータセットからバイアスを排除しつつ、合成データを作ることでプライバシーも配慮したデータセットを作るFELICIAを提案。各データ保有者内でのGANとどのデータ保有者かを判断するDiscriminatorで作成する。 t.co/G1HXLF5j1m
@AkiraTOSEI
@AkiraTOSEI t.co/HflfUsfrpG They propose FELICIA that creates dataset which eliminates bias from the data set each party has and also is privacy-aware by creating synthetic dataset in the framework of Federated Learning. t.co/3teCw4zzIc

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