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Catalogue complet

Applications of Federated Learning in
Technological Advancements

Applications of Federated Learning in Technological Advancements

Use Cases and Applications

Combining Human Thinking and Creativity with Technology to Inspire Audiences

Authentic Marketing

Discover how to use AI confidently, responsibly and ethically as an HR practitioner with this practical jargon-free guide.

Understand How to Use Artificial Intelligence in Your HR Role

AI for People Professionals

Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google's CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoreticalfoundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: <ul> <li>Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selection</li> <li>Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation</li> <li>Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI's greater impact on information integrity</li> <li>Security attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacks</li> <li>Privacy-preserving AI techniques including federated learning and homomorphic encryption</li> </ul> Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.

Triangulating AI Security

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

Methods, Applications, and Implications

Adaptive AI in Sensor Informatics

A How-To Guide for Implementation and Management

AI for Public Relations

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Applications of Federated Learning in Technological Advancements

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