Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone. (arXiv:2208.05895v1 [cs.CR])

Federated Learning (FL) opens new perspectives for training machine learning
models while keeping personal data on the users premises. Specifically, in FL,
models are trained on the users devices and only model updates (i.e.,
gradients) are sent to a central server for aggregation purposes. However, the
long list of inference attacks that leak private data from gradients, published
in the recent years, have emphasized the need of devising effective protection
mechanisms to incentivize the adoption of FL at scale. While …

arm attacks federated learning systems