Explainability in Federated Learning

"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.

Autorentext

Dr. Sravanthi Dontu and Dr. Rohith Vallabhaneni, both accomplished researchers with Ph.D.s from the University of the Cumberlands, USA, specialize in AI and IT. Their expertise spans cloud computing, cybersecurity, IoT, and software engineering. They have contributed significantly through publications, innovation, leadership, and global connects.