text: 364,151,1388033706967904258,268337552,Nicolas Kourtellis,"Preprint of our new paper proposing the 1st Privacy-Preserving Federated Learning Framework with TEEs, accepted @ACMMobiSys 2021, here: <LINK> @VincentMo6,@realhamed,@minoskt,@_EduardMarin_,@Diego_Perino Powered by @TEFresearch,@concordiah2020, @accordion_h2020 <LINK>",https://arxiv.org/abs/2104.14380,"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. ","PPFL: Privacy-preserving Federated Learning with Trusted Execution