Andere Kunden interessierten sich auch für
![Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops]( "Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops")
Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops56,99 €![Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops]( "Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops")
Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops56,99 €![Federated Distributed Systems]( "Federated Distributed Systems")
Federated Distributed Systems44,99 €![Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health]( "Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health")
Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health38,99 €![A Recommender Agent for Personalizing Learning Environments]( "A Recommender Agent for Personalizing Learning Environments")
A Recommender Agent for Personalizing Learning Environments24,99 €![DIAGONOSIS OF HEART-ABNORMALITIES DETECTION USING MACHINE LEARNING]( "DIAGONOSIS OF HEART-ABNORMALITIES DETECTION USING MACHINE LEARNING")
DIAGONOSIS OF HEART-ABNORMALITIES DETECTION USING MACHINE LEARNING26,99 €![Web Mining to Generate Multimodal Learning Materials for Children]( "Web Mining to Generate Multimodal Learning Materials for Children")
Web Mining to Generate Multimodal Learning Materials for Children24,99 €
"Explainability in Federated Learning" offers a comprehensive exploration of integrating explainable AI (XAI) into federated learning (FL) systems. The book begins by outlining the fundamentals of FL and XAI before delving into their intersection, highlighting the challenges and benefits of interpretability in decentralized environments. It presents various explainability techniques tailored to FL, emphasizing personalization, handling of heterogeneous data, and operation in resource-constrained settings. Key chapters address trust, fairness, and transparency, supported by real-world case studies and visualization tools. Ethical, legal, and social implications are discussed alongside adversarial perspectives. The book concludes with benchmarking strategies and future research directions, serving as a vital guide for researchers, developers, and policymakers aiming to build transparent, trustworthy FL models.