تقرير
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
| العنوان: | PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments |
|---|---|
| المؤلفون: | Mo, Fan, Haddadi, Hamed, Katevas, Kleomenis, Marin, Eduard, Perino, Diego, Kourtellis, Nicolas |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 15 pages, 8 figures, accepted to MobiSys 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2104.14380 |
| رقم الأكسشن: | edsarx.2104.14380 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |