Thesis Abstract

Federated learning is a machine learning approach that enables training models across decentralized devices while keeping the data on the devices. This project aims to design and implement a privacy-preserving federated learning framework to address the privacy concerns associated with traditional centralized machine learning approaches. The framework will allow multiple devices to collaboratively train a global model without sharing their raw data, thus preserving the privacy of individual users.

Thesis Overview