Differential privacy in peer-to-peer federated learning
Neural networks have become tremendously successful in recent times due to larger computing power and availability of tagged datasets for various applications. Training these networks is computationally demanding and often requires proprietary datasets to yield usable insights. In order to incent...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/165929 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Neural networks have become tremendously successful in recent times due to larger
computing power and availability of tagged datasets for various applications. Training
these networks is computationally demanding and often requires proprietary datasets to
yield usable insights. In order to incentivise stakeholders to share their datasets in order
to build stronger neural networks and protect their privacy interests, it is important to
implement differential privacy mechanisms during the training of neural networks to
protect against attacks that might expose their data to malicious agents. The objective
of this project is to study the effectiveness of differential privacy implementation on
peer-to-peer federated learning in protecting proprietary data from exposure. |
|---|