siddharthdivi/Unifying-Distillation-with-Personalization-in-Federated-Learning
Repository that contains the code for the paper titled, 'Unifying Distillation with Personalization in Federated Learning'.
This project helps machine learning researchers or practitioners working with federated learning models. It takes distributed image datasets like CIFAR-10 and MNIST to evaluate various personalized federated learning algorithms, including PersFL-KD, FedAvg, FedPer, pFedMe, and Per-FedAvg. The output consists of experimental results and performance metrics in pickle files, allowing for analysis of different model configurations.
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Use this if you are researching or implementing personalized federated learning methods and need to compare their performance on image classification tasks.
Not ideal if you are looking for a ready-to-deploy federated learning solution or a tool for non-research-oriented machine learning applications.
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Jupyter Notebook
License
MIT
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Last pushed
May 31, 2021
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