Federated Learning for Collaborative Model Training
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Motivation for Federated Learning in Decentralized Environments<br>
- 1.2Objectives of the Project<br>
- 2.Fundamentals of Federated Learning<br>
- 2.1Decentralized Model Training and Data Privacy<br>
- 2.2Federated Averaging and Model Aggregation<br>
- 3.Communication and Security in Federated Learning<br>
- 3.1Secure Aggregation Protocols<br>
- 3.2Privacy-Preserving Techniques<br>
- 4.Optimization Algorithms for Federated Learning<br>
- 4.1Federated Stochastic Gradient Descent (FSGD)<br>
- 4.2Adaptive Learning Rate Methods<br>
- 5.Applications of Federated Learning<br>
- 5.1Healthcare and Medical Imaging Analysis<br>
- 5.2Financial Data Analysis and Privacy-Preserving Models<br>
- 5.3Edge Computing and Internet of Things (IoT) Devices<br></p>
Project 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>Project Overview
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