Performance Analysis of Federated Machine Learning Frameworks

MPhil Thesis Defence


Title: "Performance Analysis of Federated Machine Learning Frameworks"

By

Mr. Qinghe JING


Abstract

The scarcity of data and isolated data islands encourage different 
organizations to share data with each other to train machine learning 
models. However, there are increasing concerns on the problems of data 
privacy and security, which urges people to seek a solution such as 
Federated Learning (FL) to share training data without violating data 
privacy. Google first introduced their solution for mobile devices in 
which users can form a federation and train a powerful model 
cooperatively, without leaking their own data. WeBank developed their 
Federated Transfer Learning (FTL) which extends FL applicable to more 
scenarios. However, the benefits come with a cost of extra computation and 
communication consumption, resulting in efficiency problems. In order to 
efficiently deploy and scale up Federated Learning solutions in production 
environment, we need a deep understanding on how the infrastructure 
affects the efficiency. This thesis tries to answer this question by 
quantitatively measuring real-word Federated Learning applications (TFF 
and FATE) on Google Cloud. According to the results of carefully designed 
experiments, we present the bottlenecks of each applications which can 
assist the future optimizations.


Date:			Wednesday, 14 August 2019

Time:			2:00pm - 4:00pm

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Dr. Kai Chen (Supervisor)
 			Dr. Qifeng Chen (Chairperson)
 			Prof. Gary Chan


**** ALL are Welcome ****