Development of a privacy-preserving federated learning framework
Table Of Contents
<p>1. Introduction<br> - Background and significance of federated learning<br> - Objectives and scope of the project<br>2. Literature Review<br> - Overview of federated learning techniques<br> - Existing frameworks and their limitations<br>3. System Architecture and Design<br> - Selection of privacy-preserving protocols<br> - Framework architecture and components<br>4. Implementation and Integration<br> - Development of the federated learning framework<br> - Integration with distributed data sources<br>5. Security and Privacy Analysis<br> - Threat modeling and risk assessment<br> - Privacy guarantees and security measures<br></p>
Thesis Abstract
<p> This project focuses on developing a privacy-preserving federated learning framework for collaborative model training across distributed data sources. The framework will enable multiple parties to jointly train machine learning models without sharing their raw data, thereby preserving data privacy and confidentiality. The project aims to address the challenges of privacy and data ownership in federated learning scenarios, particularly in healthcare, finance, and IoT domains. <br></p>Thesis Overview
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