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：基于移动设备进行模型训练的联邦学习正日益普及。当前此类系统在模型精度、隐私保障与设备效率之间存在显著权衡。例如，Oort（OSDI 2021）在精度与效率上表现优异，但需要可信中央服务器；而Orchard（OSDI 2020）虽能在不可信服务器上实现良好的精度与严格的差分隐私保障，却对设备造成巨大开销。本文提出新型联邦学习系统Aero，显著优化了这一权衡。Aero在保证良好精度与不可信服务器上差分隐私的同时，维持设备低开销。其核心思想是将系统架构与设计适配至特定主流联邦学习算法，这需要新颖的优化技术，例如用于安全聚合设备更新的新协议。实验评估表明，Aero的精度可与无差分隐私的原始联邦学习相媲美，且相较于Orchard，其（CPU与网络）效率提升高达$10^5$倍。