Explainability in Federated Learning
LAP Lambert Academic Publishing
Paperback or Softback 00116 pages Apr 22, 2025
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LAP Lambert Academic Publishing
Explainability in Federated Learning
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"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.
- Author:
- Sravanthi Dontu
- Publisher:
- LAP Lambert Academic Publishing
- Publication Date:
- Apr 22, 2025
- Number of pages:
- 00116 pages
- Binding:
- Paperback or Softback
- ISBN-10:
- 6208443415
- ISBN-13:
- 9786208443412