A study on Privacy-Preserving Federated Learning and enhancement through Transfer Learning

A study on Privacy-Preserving Federated Learning and enhancement through Transfer Learning

Mînea, Robert (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Tielman, M.L. (graduation committee)
Liang, K. (mentor)
Wang, R. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

Privacy in today's world is a very important topic and all the more important when sizeable amounts of data are needed in Neural Network processing models. Federated Learning is a technique which aims to decentralize the training process in order to allow the clients to maintain their privacy, while also contributing to a broader learning process. In order to allow parties that undertake similar tasks to share data between them, even if they don't follow the same feature representation or domain distribution, Transfer Learning is also used in order to augment the learning by sharing knowledge with the contributing parties. The name of this combination of techniques is Federated Transfer Learning. This paper aims to showcase the strengths and weaknesses of Federated Learning through a simple implementation while comparing different Federated Transfer Learning frameworks that can be used in order to enhance the capabilities of a simple federation of clients that are contributing towards the learning of a similar task.

Federated Learning
Transfer Learning
Privacy Preserving

http://resolver.tudelft.nl/uuid:4de62907-d4ea-4337-9ca3-89c078121714

Student theses

bachelor thesis

© 2021 Robert Mînea