Description
- Covers all major aspects of federated learning in various applications.
- Presents the framework and important key aspects of federated machine learning. Major use cases like healthcare, industrial automation, blockchain and IoT are discussed in this book.
- Includes a wide variety of topics, thus offers readers multiple perspectives on a variety of disciplines included in a number of chapters.
Table of Contents
Introduction to Federated Learning: Methods, and Classifications. Go Local, Go Global and Go Fusion - How to pick data from various contexts. Federated Learning Architectures, Opportunities, and Applications. Secure and Private Federated Learning through Encrypted Parameter Aggregation. Navigating Privacy Concerns in Federated Learning: A GDPR-Focused Analysis. A Federated Learning Approach for Resource-Constrained IoT Security Monitoring. Efficient Federated Learning Techniques for Data Loss Prevention in Cloud Environment. Maximizing Fog Computing Efficiency with Federated Multi-Agent Deep Reinforcement Learning. Future of Medical Research with a data-driven Federated Learning Approach. Collaborative Federated Learning in Healthcare Systems. Federated Learning for Efficient Cardiac Disease Prediction based on Hyper Spectral Feature Selection using Deep Spectral Convolution Neural Network. A Federated Learning based Alzheimer’s Disease Prediction. Detecting Device Sensors of Luxury Hotel Using Blockchain Based Federated Learning to Increase Customer Satisfaction. Navigating the Complexity of Macro-Tasks: Federated Learning as a Catalyst for Effective Crowd Coordination. Stock Market Prediction via Twitter Sentiment Analysis using BERT: A Federated Learning Approach.
