Design and implementation of a privacy-preserving federated learning framework
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Motivation<br> 1.3 Objectives<br>2. Literature Review<br> 2.1 Federated Learning<br> 2.2 Privacy-Preserving Machine Learning<br> 2.3 Related Work<br>3. System Architecture<br> 3.1 Client-Server Communication<br> 3.2 Model Aggregation<br> 3.3 Privacy-Preserving Techniques<br>4. Design and Implementation<br> 4.1 Data Encryption and Decryption<br> 4.2 Differential Privacy Mechanisms<br> 4.3 Model Aggregation Algorithms<br>5. Evaluation<br> 5.1 Privacy Analysis<br> 5.2 Performance Evaluation<br></p>
Thesis Abstract
<p> 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. <br></p>Thesis Overview
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