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