Lee-Gihun/FedNTD
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"
This is a framework for researchers and practitioners working on federated learning. It allows you to experiment with various federated learning algorithms, including FedNTD, to train machine learning models collaboratively without centralizing data. You input datasets like MNIST or CIFAR-10 and configurations for federated learning, and it outputs trained models and experiment logs. It's designed for those evaluating or implementing distributed machine learning solutions.
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Use this if you are a researcher or engineer in machine learning who needs to compare or develop federated learning algorithms for distributed model training.
Not ideal if you are looking for a pre-packaged, production-ready federated learning solution for immediate deployment without deep algorithm experimentation.
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90
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14
Language
Python
License
MIT
Category
Last pushed
Feb 24, 2023
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