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About Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
Comprehensive overview of the fledgling domain of Federated Learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications. Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of Federated Learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor IO, and more.
Detailed Information
| Author: | Pethuru Raj Chelliah |
|---|---|
| Publication Year: | 2024 |
| ISBN: | 9781394219216 |
| Language: | English |
| File Size: | 269 |
| Format: | |
| Price: | FREE |
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