Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning. (arXiv:2209.12046v1 [cs.LG])

This paper proposes a sensor data anonymization model that is trained on
decentralized data and strikes a desirable trade-off between data utility and
privacy, even in heterogeneous settings where the collected sensor data have
different underlying distributions. Our anonymization model, dubbed Blinder, is
based on a variational autoencoder and discriminator networks trained in an
adversarial fashion. We use the model-agnostic meta-learning framework to adapt
the anonymization model trained via federated learning to each user's data
distribution. We evaluate Blinder under …

end end-to-end federated learning privacy protection systems