A performance analysis of VM-based Trusted Execution Environments for Confidential Federated Learning

TL;DR

This paper compares VM-based and application-isolation TEEs for confidential federated learning, showing VM-based TEEs have minimal performance overhead, enabling secure use of untrusted environments.

Contribution

It provides the first performance analysis of VM-based TEEs versus traditional TEEs in confidential federated learning applications.

Findings

VM-based TEEs introduce at most 1.5x overhead

Secure communication with TLS adds minimal performance impact

VM-based TEEs enable secure federated learning in untrusted environments

Abstract

Federated Learning (FL) is a distributed machine learning approach that has emerged as an effective way to address recent privacy concerns. However, FL introduces the need for additional security measures as FL alone is still subject to vulnerabilities such as model and data poisoning and inference attacks. Confidential Computing (CC) is a paradigm that, by leveraging hardware-based trusted execution environments (TEEs), protects the confidentiality and integrity of ML models and data, thus resulting in a powerful ally of FL applications. Typical TEEs offer an application-isolation level but suffer many drawbacks, such as limited available memory and debugging and coding difficulties. The new generation of TEEs offers a virtual machine (VM)-based isolation level, thus reducing the porting effort for existing applications. In this work, we compare the performance of VM-based and application-isolation level TEEs for confidential FL (CFL) applications. In particular, we evaluate the impact of TEEs and additional security mechanisms such as TLS (for securing the communication channel). The results, obtained across three datasets and two deep learning models, demonstrate that the new VM-based TEEs introduce a limited overhead (at most 1.5x), thus paving the way to leverage public and untrusted computing environments, such as HPC facilities or public cloud, without detriment to performance.