# SecFL: Confidential Federated Learning using TEEs

arXiv: https://arxiv.org/abs/2110.00981 · HTML: https://mlsanity.com/paper/2110.00981/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Provably Secure Federated Learning against Malicious Clients](https://mlsanity.com/paper/2102.01854/) (2021)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [PrivacyFL: A simulator for privacy-preserving and secure federated learning](https://mlsanity.com/paper/2002.08423/) (2020)
- [TensorSCONE: A Secure TensorFlow Framework using Intel SGX](https://mlsanity.com/paper/1902.04413/) (2019)
- [Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders](https://mlsanity.com/paper/2003.14099/) (2020)
- [TEEMon: A continuous performance monitoring framework for TEEs](https://mlsanity.com/paper/2012.06554/) (2020)
- [A practical approach for updating an integrity-enforced operating system](https://mlsanity.com/paper/2101.01289/) (2021)
- [Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support](https://mlsanity.com/paper/2103.16898/) (2021)
- [WELES: Policy-driven Runtime Integrity Enforcement of Virtual Machines](https://mlsanity.com/paper/2104.14862/) (2021)

## Built on by (most cited first, 3 of 3)

- [Machine Learning with Confidential Computing: A Systematization of Knowledge](https://mlsanity.com/paper/2208.10134/) (2022)
- [VerifiableFL: Verifiable Claims for Federated Learning using Exclaves](https://mlsanity.com/paper/2412.10537/) (2024)
- [Protecting Confidentiality, Privacy and Integrity in Collaborative Learning](https://mlsanity.com/paper/2412.08534/) (2024)

## Abstract (arXiv)

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple clients to jointly train a model to take benefits from diverse datasets from the clients without sharing their local training datasets. FL helps reduce data privacy risks. Unfortunately, FL still exist several issues regarding privacy and security. First, it is possible to leak sensitive information from the shared training parameters. Second, malicious clients can collude with each other to steal data, models from regular clients or corrupt the global training model. To tackle these challenges, we propose SecFL - a confidential federated learning framework that leverages Trusted Execution Environments (TEEs). SecFL performs the global and local training inside TEE enclaves to ensure the confidentiality and integrity of the computations against powerful adversaries with privileged access. SecFL provides a transparent remote attestation mechanism, relying on the remote attestation provided by TEEs, to allow clients to attest the global training computation as well as the local training computation of each other. Thus, all malicious clients can be detected using the remote attestation mechanisms.