Explainability in Federated Learning

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Sprache: Englisch

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Beschreibung

"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.

Über den Autor

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.

Erscheinungsjahr:	2025
Fachbereich:	Anwendungs-Software
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
ISBN-13:	9786208443412
ISBN-10:	6208443415
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Dontu, Sravanthi Vallabhaneni, Rohith
Hersteller:	LAP LAMBERT Academic Publishing
Verantwortliche Person für die EU:	SIA OmniScriptum Publishing, Brivibas Gatve 197, ?-1039 Riga, customerservice@vdm-vsg.de
Maße:	220 x 150 x 7 mm
Von/Mit:	Sravanthi Dontu (u. a.)
Erscheinungsdatum:	22.04.2025
Gewicht:	0,191 kg