Encrypted Training of Deep Learning models with PyTorch + PySyft on MNIST
In this article, you are going to learn how to setup PySyft on a Raspberry PI and how to train a Recurrent Neural Network in a federated way.
Encrypted Deep Learning Classification with PyTorch & PySyft in < 33ms on MNIST
Deep Learning -> Federated Learning in 10 Lines of PyTorch + PySyft
Small but good: we only dug up one paper this week but it comes with very interesting claims.
If anyone had any doubt that private machine learning is a growing area then this might take care of that: one week with papers on MPC, HE, SGX, and DP.
While academia may still be a bit busy with submission deadlines, industry reported interesting stories this week regarding secure computation
A slightly slower period yet still new work on differential privacy and covert channels!
Good mix of approaches this time, including custom secure computation, secure enclaves, peer-to-peer gossip, and differential privacy.
Shorter but still interesting mix this week with two pillars of private machine learning: homomorphic encryption and differential privacy!
Big news this week with a good mix of everything: guides to help you explore, practical tools, and interesting new ideas! Enjoy.
This week saw updates on training logistic and boosting models on encrypted data, and an update to the student-teacher approach.
We are very happy to finally kick off our weekly dig into what's currently going on in private machine learning!
