Influence of Scalable Confidential Computing Techniques on Performance Overhead and Privacy Guarantees in Federated Learning Systems

Author

Grace Kimani, James Mwikya

Abstract

 Federated Learning (FL) has become an important distributed machine learning paradigm that enables collaborative model training while preserving data privacy by keeping data localized. Despite its advantages, “FL remains vulnerable to privacy attacks such as model inversion, gradient leakage, and membership inference attacks, particularly in untrusted multi-party environments. Confidential computing techniques including Trusted Execution Environments (TEEs), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and Confidential Virtual Machines (CVMs) have been proposed to strengthen privacy protection. However, these techniques often introduce significant computational and communication overhead, limiting their adoption in resource-constrained edge environments. This desktop review synthesizes recent literature published between 2019 and 2026 to examine the influence of scalable confidential computing techniques on performance overhead and privacy guarantees in federated learning systems. Literature was collected from Scopus, Web of Science, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, Google Scholar, and relevant grey literature using predefined inclusion and exclusion criteria. A thematic synthesis approach was employed to identify recurring patterns and research gaps. The review reveals that scalable confidential computing mechanisms can significantly improve privacy protection while reducing computational overhead through hardware-assisted security, optimized cryptographic protocols, and hybrid architectures. Nevertheless, challenges remain regarding scalability, interoperability, and deployment in heterogeneous edge computing environments. The review concludes by proposing future research directions aimed at developing adaptive confidential computing frameworks that achieve stronger privacy guarantees with minimal performance degradation.”

Keywords

Federated Learning, Confidential Computing, Trusted Execution Environments, Privacy Preservation, Performance Overhead and Edge Computing

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References

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