Towards a Practical, Trustful and Efficient Verification Framework for Vertical Federated Learning

MPhil Thesis Defence


Title: "Towards a Practical, Trustful and Efficient Verification Framework for 
Vertical Federated Learning"

By

Mr. Cengguang ZHANG


Abstract

Vertical federated learning aims to privately train collaborative machine 
learning models across data silos which contain different features for the same 
set of entities. It delicately designs secure protocols among participants to 
prevent data leakage from intermediate results in the federated process. These 
protocols have proven to be secure when all participants are semi-honest. 
However, we reveal that that when some internal roles or components of these 
participants are comprised, the VFL systems is easy to be attacked. In this 
paper, we take the initiative to solve this problem by proposing Aegis, a 
practical, trustful and efficient verification framework. Aegis is a gateway 
plus end host solution to be trustful when the end host components are 
compromised. By combining both online and offline verification, Aegis is highly 
efficient and can verify if the VFL system is under attacks as early as 
possible. Furthermore, Aegis is fully compatible with existing VFL systems 
without modifying any existing VFL protocols. We implement Aegis with FATE and 
evaluate Aegis with real-world VFL algorithms and datasets. Evaluation results 
show that Aegis can detect 88.9% attacks with 1) online verification by adding 
< 0.1% total time and 2) offline verification by reducing up to 63.27% task 
running time.


Date:  			Tuesday, 31 August 2021

Time:			10:00am - 12:00noon

Zoom meeting:
https://hkust.zoom.us/j/93780940061?pwd=VVQ3UWRheWJvd05BRWdMZTBnM25odz09

Committee Members:	Dr. Kai Chen (Supervisor)
 			Dr. Qifeng Chen (Chairperson)
 			Prof. Qiong Luo


**** ALL are Welcome ****