AshwinRJ/Federated-Learning-PyTorch
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
This project helps machine learning engineers and researchers explore federated learning, a technique for collaboratively training models without centralizing user data. You can feed in image datasets like MNIST or CIFAR-10 and observe how different configurations of federated learning impact model accuracy, particularly when data is distributed unevenly among users. It's designed for those who need to understand or implement decentralized machine learning approaches.
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Use this if you are a machine learning researcher or practitioner interested in experimenting with federated learning architectures and evaluating their performance under various data distribution conditions.
Not ideal if you need a production-ready federated learning system or a tool for highly complex, real-world deployment scenarios.
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Python
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MIT
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Last pushed
May 07, 2024
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