# Flatee: Federated Learning Across Trusted Execution Environments

arXiv: https://arxiv.org/abs/2111.06867 · HTML: https://mlsanity.com/paper/2111.06867/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2021.

## Built on by (most cited first, 5 of 5)

- [BAFFLE: A Baseline of Backpropagation-Free Federated Learning](https://mlsanity.com/paper/2301.12195/) (2023)
- [Noiseless Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2404.09536/) (2024)
- [SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation](https://mlsanity.com/paper/2201.07730/) (2022)
- [FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning](https://mlsanity.com/paper/2206.03852/) (2022)
- [FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks](https://mlsanity.com/paper/2310.02113/) (2023)

## Abstract (arXiv)

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).