Federated Learning: A Comprehensive Overview of Methods and Applications

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ASIN ‏ : ‎ B0B62RZ5LS
Publisher ‏ : ‎ Springer (July 7, 2022)
Publication date ‏ : ‎ July 7, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 53295 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 919 pages

Federated Learning: A Comprehensive Overview of Methods and Applications

Federated learning is a cutting-edge machine learning technique that allows multiple parties to collaboratively train a shared model without sharing their raw data. This decentralized approach to training models has gained popularity in recent years due to its privacy-preserving capabilities and scalability. In this post, we will provide a comprehensive overview of federated learning, including its methods and applications.

Methods of Federated Learning:

1. Horizontal Federated Learning: In this approach, multiple clients have access to the same features but have different samples of data. The clients train a shared model by averaging their local updates.

2. Vertical Federated Learning: In this approach, clients have different features but share a common set of samples. The clients train separate models on their respective features and combine them to create a global model.

3. Federated Transfer Learning: This method involves transferring knowledge from a pre-trained model to a federated learning setting, where clients fine-tune the model on their local data.

Applications of Federated Learning:

1. Healthcare: Federated learning is widely used in healthcare for training models on sensitive patient data without compromising privacy. Hospitals can collaborate to train models for disease diagnosis, drug discovery, and personalized treatment recommendations.

2. Finance: Banks and financial institutions can use federated learning to build fraud detection models while maintaining the privacy of customer transaction data. By aggregating local updates from multiple banks, a robust fraud detection model can be trained.

3. Internet of Things (IoT): Federated learning is ideal for training models on data generated by IoT devices, such as smart sensors and wearable devices. By training models locally on the devices and aggregating the updates, personalized recommendations and predictive maintenance strategies can be implemented.

4. Edge Computing: Federated learning is well-suited for edge computing environments where data is generated and processed locally on devices. By training models on the edge devices and only sending model updates to a central server, real-time predictions can be made without compromising data privacy.

In conclusion, federated learning offers a promising solution for collaborative model training while preserving data privacy and security. By understanding the various methods and applications of federated learning, organizations can leverage this technique to build robust and scalable machine learning models.
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