Songyue-Guo/FedGR
DASFAA2023 FedGR Code Repository. Federated learning for double unbalance settings (sample quantities imbalance for different classes in client and label or class imbalance for different client cross-client)
This is a federated learning framework designed for machine learning researchers and practitioners who build models using data distributed across multiple devices or organizations. It addresses common challenges where data imbalances exist both within each client's dataset (e.g., more images of cats than dogs) and across different clients (e.g., some clients have very few examples of a specific class). You input distributed datasets with these double imbalance characteristics, and it outputs improved, more accurate federated learning models.
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Use this if you are developing federated learning models and frequently encounter situations where your training data is unevenly distributed across clients and also imbalanced in terms of class representation within each client's dataset.
Not ideal if your machine learning problem does not involve federated learning, or if your distributed data is well-balanced across clients and within each client's specific classes.
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41
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4
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 26, 2023
Commits (30d)
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