AshwinRJ/Federated-Learning-PyTorch
A PyTorch implementation of federated learning for distributed training of deep neural networks on decentralized data.
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This repository implements the seminal federated learning paper for training deep networks from decentralized data. It provides both baseline and federated training approaches on standard datasets (MNIST, Fashion MNIST, CIFAR10) using simple MLP and CNN models. The implementation supports both IID and non-IID data distributions, allowing users to simulate realistic federated scenarios where client data may be heterogeneous.
Frequently asked
- What is AshwinRJ/Federated-Learning-PyTorch?
- A PyTorch implementation of federated learning for distributed training of deep neural networks on decentralized data.
- Is Federated-Learning-PyTorch open source?
- Yes — AshwinRJ/Federated-Learning-PyTorch is open source, released under the MIT license.
- What language is Federated-Learning-PyTorch written in?
- AshwinRJ/Federated-Learning-PyTorch is primarily written in Python.
- How popular is Federated-Learning-PyTorch?
- AshwinRJ/Federated-Learning-PyTorch has 1.4k stars on GitHub.
- Where can I find Federated-Learning-PyTorch?
- AshwinRJ/Federated-Learning-PyTorch is on GitHub at https://github.com/AshwinRJ/Federated-Learning-PyTorch.