We propose and implement a Privacy-preserving Federated Learning ($PPFL$)
framework for mobile systems to limit privacy leakages in federated learning.
Leveraging the widespread presence of Trusted Execution Environments (TEEs) in
high-end and mobile devices, we utilize TEEs on clients for local training, and
on servers for secure aggregation, so that model/gradient updates are hidden
from adversaries. Challenged by the limited memory size of current TEEs, we
leverage greedy layer-wise training to train each model's layer inside the
trusted area until its convergence. The performance evaluation of our
implementation shows that $PPFL$ can significantly improve privacy while
incurring small system overheads at the client-side. In particular, $PPFL$ can
successfully defend the trained model against data reconstruction, property
inference, and membership inference attacks. Furthermore, it can achieve
comparable model utility with fewer communication rounds (0.54$\times$) and a
similar amount of network traffic (1.002$\times$) compared to the standard
federated learning of a complete model. This is achieved while only introducing
up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in
$PPFL$'s client-side.

该论文提出并实现了一种基于隐私保护联合学习（PPFL）的框架，旨在通过利用受信任的执行环境（TEEs）对客户端进行本地训练，通过 TEEs 对服务器进行安全聚合，从而在联合学习中限制隐私泄漏，并达到显著的隐私保护与可比较的模型效用提升。