Evaluating differential privacy on language processing federated learning

Evaluating differential privacy on language processing federated learning

Van Opstal, Quinten (TU Delft Electrical Engineering, Mathematics and Computer Science)

Huang, J. (mentor)
Chen, Lydia Y. (mentor)
Zuniga, Marco (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2024-02-02

Federated learning provides a lot of opportunities, especially with the built-in privacy considerations. There is however one attack that might compromise the utility of federated learning: backdoor attacks [14]. There are already some existing defenses, like flame [13] but they are computationally expensive [14]. This paper evaluates a version of differential privacy, where the Gaussian noise added to the aggravated model of the clipped updates is smaller than usually. This is often referred to as weak differential privacy or weakDP. This paper evaluates weakDP with different parameters to find if weakDP can be used as a defense for a language processing federated learning classifier against a backdoor attack.

Federated learning
Differential Privacy
Non-iid

http://resolver.tudelft.nl/uuid:7ed823a0-5a2c-4439-8fd0-60e02235f24e

A link to the associated github project. - https://github.com/QuintenVanOpstal/OOD_Federated_Learning.git

Student theses

bachelor thesis

© 2024 Quinten Van Opstal