  Federated learning becomes a prominent approach when different entities want
to learn collaboratively a common model without sharing their training data.
However, Federated learning has two main drawbacks. First, it is quite
bandwidth inefficient as it involves a lot of message exchanges between the
aggregating server and the participating entities. This bandwidth and
corresponding processing costs could be prohibitive if the participating
entities are, for example, mobile devices. Furthermore, although federated
learning improves privacy by not sharing data, recent attacks have shown that
it still leaks information about the training data. This paper presents a novel
privacy-preserving federated learning scheme. The proposed scheme provides
theoretical privacy guarantees, as it is based on Differential Privacy.
Furthermore, it optimizes the model accuracy by constraining the model learning
phase on few selected weights. Finally, as shown experimentally, it reduces the
upstream and downstream bandwidth by up to 99.9% compared to standard federated
learning, making it practical for mobile systems.
