Federated Learning for Collaborative Model Training
Table Of Contents
<p>1. Introduction<br> 1.1 Motivation for Federated Learning in Decentralized Environments<br> 1.2 Objectives of the Project<br>2. Fundamentals of Federated Learning<br> 2.1 Decentralized Model Training and Data Privacy<br> 2.2 Federated Averaging and Model Aggregation<br>3. Communication and Security in Federated Learning<br> 3.1 Secure Aggregation Protocols<br> 3.2 Privacy-Preserving Techniques<br>4. Optimization Algorithms for Federated Learning<br> 4.1 Federated Stochastic Gradient Descent (FSGD)<br> 4.2 Adaptive Learning Rate Methods<br>5. Applications of Federated Learning<br> 5.1 Healthcare and Medical Imaging Analysis<br> 5.2 Financial Data Analysis and Privacy-Preserving Models<br> 5.3 Edge Computing and Internet of Things (IoT) Devices<br></p>
Thesis Abstract
<p> Federated learning has emerged as a promising approach for training machine learning models across decentralized devices while preserving data privacy. This project aims to investigate the principles and applications of federated learning in collaborative model training. The project will explore the architecture of federated learning systems, communication protocols, and optimization algorithms for model aggregation. Additionally, the project will analyze the potential applications of federated learning in healthcare, finance, and edge computing environments. <br></p>Thesis Overview
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