shaoxiongji/federated-learning
A PyTorch Implementation of Federated Learning
This project helps machine learning researchers and practitioners explore federated learning, a technique for training models on decentralized data without moving the data itself. You input image datasets like MNIST or CIFAR-10, and it outputs performance metrics (accuracy) for federated models. This is ideal for those studying privacy-preserving machine learning or distributed model training.
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Use this if you need to experiment with or reproduce federated learning techniques for image classification on standard datasets like MNIST and CIFAR-10.
Not ideal if you require parallel computing for faster execution or need to apply federated learning to custom datasets or more complex model architectures.
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Python
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MIT
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Last pushed
Jul 25, 2024
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