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krishnan s handbook federated learning advances app opportunities 2024

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Textbook in PDF format Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated Learning has developed as a model of education in these situations. Federated Learning (FL) is an expert form of decentralized Machine Learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of Artificial Intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional Machine Learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional Machine Learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in Federated Learning for various aspects. Federated Learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. Federated Learning (FL) is a Distributed Machine Learning model that has been used in many applications today. It has been pioneered by companies like Google Inc and Apple Inc as a technique to create Machine Learning models with improved performance on distributed datasets without sacrificing privacy. Currently, federated learning is used by Apple Inc to enhance the accuracy of Face ID and Siri, and by Google to power keyboard predictions in Gboard. Its ability to handle the privacy issues raised by conventional ML models contributed to its appeal. As a result, Federated Learning is currently one of the most intriguing and promising areas in AI. It has lately altered how we gather, examine, and use data. Federated Learning has widespread applications in Industry 4.0, Healthcare and Robotics, etc. This book provides a comprehensive approach in FL for various aspects. The book comprises four sections namely Concepts and Types, Learning Process and Optimization, Tools and Techniques and Applications and use cases. The purpose of this edited book is to present a detailed explanation about concepts, tools and techniques in various applications with real time use cases

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Krishnan S. Handbook of AI in Biomedical Engineering 2021.pdf 11.7 MB
Krishnan S. Handbook on Federated Learning. Advances, App and Opportunities 2024.pdf 16.5 MB

Details for: Krishnan S. Handbook on Federated Learning. Advances, App and Opportunities 2024

krishnan s handbook federated learning advances app opportunities 2024

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