GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.
Fine-tuning is fine in federated learning
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Distributed Deep Variational Approach for Privacy-preserving Data Release
GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.