Tag: Federated

December 11, 2024

Federated Learning for Smart Communication using IoT Application (Chapman & HallCRC Cyber-Physical Systems)

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Free Download Federated Learning for Smart Communication using IoT Application (Chapman & Hall/CRC Cyber-Physical Systems) by Kaushal Kishor, Parma Nand, Vishal Jain
English | October 30, 2024 | ISBN: 1032788127 | 274 pages | MOBI | 6.05 Mb
The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.

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September 28, 2024

Federated Learning and Privacy-preserving RAGs

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Free Download Federated Learning and Privacy-preserving RAGs
Published 9/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 48 KHz
Language: English | Size: 31.15 MB | Duration: 16m 1s
Learn federated learning and privacy-preserving techniques. This course will teach you how to architect AI solutions while ensuring data privacy in Retrieval-Augmented Generation (RAG) systems.

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September 16, 2024

Advancing Software Engineering Through AI, Federated Learning, and Large Language Models

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Free Download Advancing Software Engineering Through AI, Federated Learning, and Large Language Models by Avinash Kumar Sharma, Nitin Chanderwal, Amarjeet Prajapati
English | May 2, 2024 | ISBN: 836934875X | 384 pages | PDF | 8.81 Mb
The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.

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April 25, 2024

Handbook on Federated Learning Advances, Applications and Opportunities

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Free Download Handbook on Federated Learning: Advances, Applications and Opportunities by Saravanan Krishnan, A. Jose Anand, R. Srinivasan
English | December 15, 2023 | ISBN: 103247162X | 356 pages | MOBI | 17 Mb
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.

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