# Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning

arXiv: https://arxiv.org/abs/2108.06958 · HTML: https://mlsanity.com/paper/2108.06958/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models](https://mlsanity.com/paper/2001.03274/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease](https://mlsanity.com/paper/2312.10237/) (2023)

## Abstract (arXiv)

Vertical federated learning (VFL) leverages various privacy-preserving algorithms, e.g., homomorphic encryption or secret sharing based SecureBoost, to ensure data privacy. However, these algorithms all require a semi-honest secure definition, which raises concerns in real-world applications. In this paper, we present Aegis, a trusted, automatic, and accurate verification framework to verify the security of VFL jobs. Aegis is separated from local parties to ensure the security of the framework. Furthermore, it automatically adapts to evolving VFL algorithms by defining the VFL job as a finite state machine to uniformly verify different algorithms and reproduce the entire job to provide more accurate verification. We implement and evaluate Aegis with different threat models on financial and medical datasets. Evaluation results show that: 1) Aegis can detect 95% threat models, and 2) it provides fine-grained verification results within 84% of the total VFL job time.