  Federated learning for training models over mobile devices is gaining
popularity. Current systems for this task exhibit significant trade-offs
between model accuracy, privacy guarantee, and device efficiency. For instance,
Oort (OSDI 2021) provides excellent accuracy and efficiency but requires a
trusted central server. On the other hand, Orchard (OSDI 2020) provides good
accuracy and the rigorous guarantee of differential privacy over an untrusted
server, but creates huge overhead for the devices. This paper describes Aero, a
new federated learning system that significantly improves this trade-off. Aero
guarantees good accuracy, differential privacy over an untrusted server, and
keeps the device overhead low. The key idea of Aero is to tune system
architecture and design to a specific set of popular, federated learning
algorithms. This tuning requires novel optimizations and techniques, e.g., a
new protocol to securely aggregate updates from devices. An evaluation of Aero
demonstrates that it provides comparable accuracy to plain federated learning
(without differential privacy), and it improves efficiency (CPU and network)
over Orchard by up to $10^5\times$.
