chamathpali/FedSim
Similarity Guided Model Aggregation for Federated Learning
This project helps machine learning engineers improve the accuracy of their federated learning models. It takes distributed datasets and model updates from many client devices and produces a more robust, globally aggregated model. Machine learning engineers and researchers working with federated learning architectures would find this tool useful.
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Use this if you need to aggregate machine learning models from multiple decentralized sources and want to improve the overall model's accuracy compared to standard federated averaging methods.
Not ideal if you are working with traditional centralized machine learning models or do not have a federated learning setup.
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Language
Python
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Last pushed
Mar 22, 2022
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