{
"item_title" : "Federated Learning Systems",
"item_author" : [" Muhammad Habib Ur Rehman", "Mohamed Medhat Gaber "],
"item_description" : "Federated Learning Research: Trends and Bibliometric Analysis.- A Review of Privacy-preserving Federated Learning for the Internet-of-Things.- Differentially Private Federated Learning: Algorithm, Analysis and Optimization.- Advancements of federated learning towards privacy preservation: from federated learning to split learning.- PySyft: A Library for Easy Federated Learning.- Federated Learning Systems for Healthcare: Perspective and Recent Progress.- Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems.- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies.",
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Federated Learning Systems : Towards Next-Generation AI
Overview
Federated Learning Research: Trends and Bibliometric Analysis.- A Review of Privacy-preserving Federated Learning for the Internet-of-Things.- Differentially Private Federated Learning: Algorithm, Analysis and Optimization.- Advancements of federated learning towards privacy preservation: from federated learning to split learning.- PySyft: A Library for Easy Federated Learning.- Federated Learning Systems for Healthcare: Perspective and Recent Progress.- Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems.- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies.
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Details
- ISBN-13: 9783030706036
- ISBN-10: 3030706036
- Publisher: Springer
- Publish Date: June 2021
- Dimensions: 9.21 x 6.14 x 0.5 inches
- Shipping Weight: 1.05 pounds
- Page Count: 196
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