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 $10^5\times$.

翻译：联邦学习在移动设备上训练模型的方法日益流行。当前系统在该任务中，在模型精度、隐私保证和设备效率之间表现出显著的权衡。例如，Oort（OSDI 2021）提供了出色的精度和效率，但需要可信的中央服务器。另一方面，Orchard（OSDI 2020）在不受信任的服务器上提供了良好的精度和严格的差分隐私保证，但给设备带来了巨大开销。本文介绍了Aero，一种显著改善这种权衡的新型联邦学习系统。Aero保证了良好的精度、在不受信任服务器上的差分隐私，并保持设备开销较低。Aero的关键思想是将系统架构和设计调整为一组特定的流行联邦学习算法。这种调整需要新颖的优化和技术，例如一种用于安全聚合设备更新的新协议。对Aero的评估表明，它提供了与普通联邦学习（无差分隐私）相当的精度，并且相比Orchard将效率（CPU和网络）提高了高达$10^5$倍。