Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies (Security)

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Publisher ‏ : ‎ The Institution of Engineering and Technology (October 15, 2024)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 285 pages
ISBN-10 ‏ : ‎ 1839539453
ISBN-13 ‏ : ‎ 978-1839539459
Item Weight ‏ : ‎ 1.28 pounds
Dimensions ‏ : ‎ 6.14 x 0.69 x 9.21 inches

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Split Federated Learning for Secure IoT Applications: Concepts, Frameworks, Applications, and Case Studies (Security)

In the era of Internet of Things (IoT) where billions of devices are connected and exchanging data, security and privacy are of utmost importance. Federated Learning has emerged as a promising technique for training machine learning models on decentralized data sources while preserving data privacy. However, traditional Federated Learning approaches still pose security risks, especially in the context of IoT applications where devices may be vulnerable to attacks.

To address these security concerns, Split Federated Learning has been proposed as a more secure alternative. In Split Federated Learning, the model is split into multiple parts, and each part is trained on a different subset of the data. This ensures that no single entity has access to the entire model or dataset, enhancing security and privacy.

In this post, we will delve into the concepts, frameworks, applications, and case studies of Split Federated Learning for secure IoT applications:

1. Concepts: We will explore the underlying principles of Split Federated Learning, including how the model is split, how training is coordinated, and how the final model is aggregated.

2. Frameworks: We will discuss existing frameworks and tools that support Split Federated Learning, such as PySyft and TensorFlow Federated, and how they can be used in IoT applications.

3. Applications: We will showcase real-world applications of Split Federated Learning in securing IoT devices and data, such as anomaly detection, predictive maintenance, and personalized recommendations.

4. Case Studies: We will present case studies of organizations that have successfully implemented Split Federated Learning for secure IoT applications, highlighting the benefits and challenges they faced.

Overall, Split Federated Learning holds great promise in enhancing security and privacy in IoT applications. By understanding its concepts, leveraging frameworks, exploring applications, and learning from case studies, organizations can effectively implement this technique to protect their IoT devices and data. Stay tuned for more insights on Split Federated Learning for secure IoT applications.
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