Federated Learning (FL) is a framework where one trains a single ML model on distinct datasets that cannot be gathered in a single central location. This enables companies and institutions to comply with regulations related to data location and data access while allowing for innovation and personalization.
This repo provides some code samples for running a federated learning pipeline in the Azure Machine Learning platform.
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Please also check our industry use cases below.
| Medical Imaging | Named Entity Recognition | Fraud Detection |
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| pneumonia.md | ner.md | ccfraud.md |
Please find a full documentation of this project here.
Please check the troubleshooting guide for possible solutions. If you are unable to find a solution, please open an issue in this repository.
If you have any feature requests, technical questions, or find any bugs, please do not hesitate to reach out to us.
For bug reports and feature requests, you are welcome to open an issue.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
To contribute, please start by creating a self-assigned issue giving a high-level overview of what you'd like to do. Once any discussion there concludes, follow up with a PR.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.