Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications

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ASIN ‏ : ‎ B0DN34KH7Y
Publisher ‏ : ‎ Wiley-IEEE Press; 1st edition (November 13, 2024)
Publication date ‏ : ‎ November 13, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 31042 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 398 pages
Page numbers source ISBN ‏ : ‎ 1394219210

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In the rapidly evolving field of artificial intelligence, model optimization is crucial for improving the efficiency and performance of AI models, especially in edge computing scenarios where resources are limited. One promising approach to model optimization is federated learning, which allows AI models to be trained across multiple decentralized devices without the need to transfer raw data to a central server.

In this post, we will explore the various methods and techniques for optimizing AI models using federated learning architectures, frameworks, and applications. We will discuss the benefits and challenges of federated learning, as well as the different frameworks and tools available for implementing federated learning in edge AI scenarios.

We will also highlight some real-world applications of federated learning, such as personalized healthcare, autonomous vehicles, and smart cities, where federated learning can be used to improve model performance while respecting data privacy and security.

Overall, this post will provide a comprehensive overview of model optimization methods for efficient and edge AI using federated learning architectures, frameworks, and applications, and will showcase the potential of federated learning to revolutionize the way AI models are trained and deployed in edge computing environments.
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