Lee-Gihun/FedSOL
(CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning"
This project helps machine learning researchers and practitioners evaluate and compare federated learning algorithms. You provide datasets like MNIST or CIFAR-10 and various configuration parameters for client-server communication and model training. The output is a set of experimental results demonstrating the performance and stability of different federated learning approaches, particularly FedSOL, under various conditions. This is for those studying or implementing decentralized machine learning models.
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Use this if you are a machine learning researcher or engineer working on federated learning and need a tool to run controlled experiments comparing different algorithms on standard datasets.
Not ideal if you are looking for a plug-and-play federated learning solution for production or if your primary goal is to train a single model without extensive algorithmic comparison.
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Language
Python
License
MIT
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Last pushed
Jun 28, 2024
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