**Unlock Faster Federated Learning with Client-Side Model Pr

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Unlock Faster Federated Learning with Client-Side Model Pruning

Federated learning has revolutionized the way we collaborate on machine learning models, enabling the sharing of knowledge across diverse datasets while preserving user privacy. However, as the number of participating devices grows, so does the communication overhead, which can significantly hinder the performance of federated learning. To overcome this challenge, we propose incorporating Client-Side Model Pruning into the federated learning pipeline.

What is Client-Side Model Pruning?

Client-Side Model Pruning is a technique that reduces the size of a neural network model, making it more compact and efficient. By pruning unnecessary weights and connections, the model becomes lighter, reducing the communication overhead during model upload to the server. This, in turn, speeds up the federated learning process, allowing for faster convergence and more efficient collaboration.

How Does it Work?

Here's...

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