federated learning

In federated learning many different computers work on portions of training data during machine learning and then pool parts or updates to a single resulting model. This has been applied particularly to various forms of neural network, to enhance privacy where personal data is kept in indivdiual's own devices and only weight updates generated by backprop shared back to the central model. However, it has been shown that this can still be susceptable to {[adversarial attacks}}.