  Federated machine learning leverages edge computing to develop models from
network user data, but privacy in federated learning remains a major challenge.
Techniques using differential privacy have been proposed to address this, but
bring their own challenges -- many require a trusted third party or else add
too much noise to produce useful models. Recent advances in \emph{secure
aggregation} using multiparty computation eliminate the need for a third party,
but are computationally expensive especially at scale. We present a new
federated learning protocol that leverages a novel differentially private,
malicious secure aggregation protocol based on techniques from Learning With
Errors. Our protocol outperforms current state-of-the art techniques, and
empirical results show that it scales to a large number of parties, with
optimal accuracy for any differentially private federated learning scheme.
