# TEE-based Selective Testing of Local Workers in Federated Learning Systems

arXiv: https://arxiv.org/abs/2111.02662 · HTML: https://mlsanity.com/paper/2111.02662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices](https://mlsanity.com/paper/2406.00586/) (2024)

## Abstract (arXiv)

This paper considers a federated learning system composed of a central coordinating server and multiple distributed local workers, all having access to trusted execution environments (TEEs). In order to ensure that the untrusted workers correctly perform local learning, we propose a new TEE-based approach that also combines techniques from applied cryptography, smart contract and game theory. Theoretical analysis and implementation-based evaluations show that, the proposed approach is secure, efficient and practical.