Fwd: 🎓 Call for Book Chapters 📖 Federated Learning for Privacy-Preserving Intelligence Across IoT, Healthcare, and Smart Cities 📝 Publisher: Bentham Science

[Shweta Sharma]

---------- Forwarded message ---------
From: Meenakshi Mittal <meen...@cup.edu.in>
Date: Wed, 3 Dec 2025, 20:22
Subject: Fwd: 🎓 Call for Book Chapters 📖 Federated Learning for Privacy-Preserving Intelligence Across IoT, Healthcare, and Smart Cities 📝 Publisher: Bentham Science
To: shweta sharma <shweta...@nitkkr.ac.in>

Dear Sir/Madam, Greetings of the Day!

We invite original (unpublished) research contributions for the book “Federated Learning for Privacy-Preserving Intelligence Across IoT, Healthcare, and Smart Cities”. As an editor, it is indeed a matter of great pleasure and privilege to invite you all to submit a book chapter.

CALL FOR BOOK CHAPTERS

🎓 Call for Book Chapters
 📖 Federated Learning for Privacy-Preserving Intelligence Across IoT, Healthcare, and Smart Cities

📝 Publisher: Bentham Science
📅Important Dates

·       Abstract Submission Deadline: 25^th December 2025

·       Notification of Acceptance: 20^th January 2026

·       Full Chapter Submission: 20^th March 2026

·       Final Acceptance Notification: 10^th April 2026

·       Camera Ready Chapter Submission: 10^th May 2026

 

📧 Submit abstracts & chapters to:
 👉 bentham....@gmail.com

 

Series Editors: Dr. Meenakshi Mittal, Dr. Garima Mathur, Dr. Shakeel Ahmed

 

About the book: This book explores how federated learning enables model training locally at each data owner’s site without sharing sensitive data, thereby ensuring data privacy and security. It also integrates several privacy-enhancing techniques, promotes collaboration, and fosters a secure and efficient distributed learning environment. This book discusses the foundations of federated learning, real-world applications, and ethical perspectives for building secure and scalable systems.  

Recommendation Topics but not Limited

Part I – Fundamentals of Federated Learning

1. Introduction to Federated Learning and Decentralized AI
2. Architectures of Federated Learning: Horizontal, Vertical, and Transfer Approaches
3. Federated Aggregation Algorithms and Communication Efficiency
4. Privacy-Preserving Mechanisms in Federated Learning
5. Comparative Analysis of Existing Frameworks: FedAvg, NVIDIA Clara, OpenFL, Flower
6. Security and Data Governance in Federated Systems: GDPR/HIPAA compliance, and governance frameworks.

Part II – Applications and Case Studies

6. Federated Learning in Healthcare and Medical Research: Case studies in medical collaboration and diagnostics
7. Federated Learning in Smart Cities: Privacy-aware data analytics and infrastructure security
8. Federated Learning in IoT and Cyber-Physical Systems
9. Real-World Applications and Comparative Analysis
10. Cross-Institutional Collaboration through Federated Learning: Research synergy and shared innovation

Part III – Ethical and Future Perspectives

13. Ethical, Legal, and Social Implications of Federated AI
14. Future Trends and Open Research Challenges 

 

 

With Regards

-- 

मीनाक्षी/ Meenakshi

सह-आचार्य / Associate Professor 

कंप्यूटर विज्ञान एवं प्रौद्योगिकी केंद्र/ Computer Science and Technology

इंजीनियरिंग एवं प्रौद्योगिकी विद्यापीठ/ School of Engineering & Technology

पंजाब केन्द्रीय विश्वविद्यालय/ Central University of Punjab

बठिण्डा / Bathinda - 151001

Contact no. 9417436344