Opening book details…
Can I read Federated Learning Systems: Towards Next-Generation AI 1 on EtoBox?
Federated Learning Systems: Towards Next-Generation AI 1 by Muhammad Habib ur Rehman,Mohamed Medhat Gaber (eds.) is a nonfiction available to read on EtoBox.
What is Federated Learning Systems: Towards Next-Generation AI 1 about?
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data. Erscheinungsdatum: 12.06.2021
Who reads Federated Learning Systems: Towards Next-Generation AI 1?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Muhammad Habib ur Rehman,Mohamed Medhat Gaber (eds.)
- Publisher
- Springer International Publishing : Imprint: Springer
- Published
- 2021
- Language
- EN
- ISBN
- 9783030706036
- Category
- nonfiction
- Subjects
- Engineering, Mathematics, Computer Science
Other editions & translations
More by Muhammad Habib ur Rehman,Mohamed Medhat Gaber (eds.)
Browse all works by Muhammad Habib ur Rehman,Mohamed Medhat Gaber (eds.)
Similar books
- Federated Learning : Fundamentals and Advances — Yaochu Jin & Hangyu Zhu & Jinjin Xu & Yang Chen (2023)
- Federated Learning Systems: Towards Privacy - Preserving Distributed AI — Muhammad Habib ur Rehman; Mohamed Medhat Gaber (2025)
- Federated Learning : Principles, Paradigms, and Applications — Jayakrushna Sahoo & Mariya Ouaissa & Akarsh K. Nair (2024)
- Machine Learning Evaluation: Towards Reliable and Responsible AI — Zois Boukouvalas Nathalie Japkowicz (2024)
- Modeling of Next Generation Digital Learning Environments: Complex Systems Theory — Marc Trestini
- Communication Efficient Federated Learning for Wireless Networks — Shuguang Cui Mingzhe Chen (2024)