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Synopsis
"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.
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Explainability in Federated Learning
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Explainability in Federated Learning
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9786208443412
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Explainability in Federated Learning
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Explainability in Federated Learning
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Omniscriptum, LAP Lambert Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -'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. 116 pp. Englisch. Seller Inventory # 9786208443412
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Explainability in Federated Learning
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - '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. Seller Inventory # 9786208443412
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Explainability in Federated Learning
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Explainability in Federated Learning
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Explainability in Federated Learning
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LAP LAMBERT Academic Publishing Apr 2025, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -'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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch. Seller Inventory # 9786208443412
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Explainability in Federated Learning
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ISBN 10: 6208443415
ISBN 13: 9786208443412
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Taschenbuch. Condition: Neu. Explainability in Federated Learning | Sravanthi Dontu (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208443412 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 133335967
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Explainability in Federated Learning
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208443415
ISBN 13: 9786208443412
New
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Seller: Biblios, Frankfurt am main, HESSE, Germany
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Condition: New. PRINT ON DEMAND. Seller Inventory # 18404461257