Thursday 23 January 2025
As technology continues to advance, our reliance on artificial intelligence and machine learning has become increasingly important in various aspects of our lives. However, these advancements also raise concerns about data privacy and security. A new study aims to address this issue by exploring the use of Trusted Execution Environments (TEEs) for confidential federated learning.
Federated learning is a method that enables multiple devices or organizations to jointly train an artificial intelligence model without sharing their individual data. This approach has gained popularity in recent years due to its ability to preserve data privacy while still achieving accurate results. However, as the number of participants increases, so does the risk of security breaches and data tampering.
To mitigate these risks, researchers have turned to TEEs, which provide a secure environment for computing and storing sensitive information. Intel’s Software Guard Extensions (SGX) is one such technology that has been widely used in this context. However, SGX has some limitations, including restricted access to hardware resources and potential vulnerabilities.
The new study investigates the use of VM-based TEEs, which offer a more flexible and secure solution for confidential federated learning. Intel’s Trust Domain Extensions (TDX) is one such technology that provides VM-level isolation, allowing multiple applications to run securely within a single virtual machine.
To evaluate the performance of TDX, researchers conducted extensive experiments using three different datasets and two deep learning models. The results showed that TDX introduces minimal overhead in terms of execution time, with some scenarios even showing improved performance compared to SGX.
The study also explored the impact of adding secure communication mechanisms, such as Transport Layer Security (TLS), on the overall performance. The results indicated that TLS adds a significant delay, but this can be mitigated by optimizing the implementation and reducing the number of network requests.
Overall, the study demonstrates the potential of VM-based TEEs for confidential federated learning. By providing a secure environment for computing and storing sensitive information, these technologies can help to preserve data privacy while still achieving accurate results.
The findings have significant implications for various industries that rely heavily on AI and machine learning, including healthcare, finance, and education. As the demand for secure and private artificial intelligence solutions continues to grow, this study provides valuable insights into the development of more effective and efficient TEEs.
In addition to its practical applications, the study also contributes to a deeper understanding of the underlying technology and its limitations.
Cite this article: “Secure Federated Learning with VM-Based Trusted Execution Environments”, The Science Archive, 2025.
Artificial Intelligence, Machine Learning, Data Privacy, Security, Trusted Execution Environments, Confidential Federated Learning, Intel Sgx, Vm-Based Tees, Trust Domain Extensions, Tls
