adap/flower
Sep 2025 – Sep 2025
1 Month active
Languages Used
MarkdownPythonRSTShellTOMLreStructuredTextrst
Technical Skills
API DesignAPI RefactoringBackend DevelopmentCode ClarityCode RefactoringConfiguration Management
Yan Gao
PROFILE
Yan Gao
Yan contributed to the adap/flower repository by delivering a refreshed federated learning starter kit and modernizing FlowerTune examples. Using Python and PyTorch, Yan updated XGBoost federated templates to integrate the FedXgbBagging strategy, refined configuration management, and improved data handling for smoother onboarding. The work included refactoring general templates and tabular examples, updating dependencies, and enhancing code clarity for maintainability. Yan also strengthened documentation with detailed upgrade guides and tutorials, and addressed robustness in federated learning strategies by fixing FedAvg evaluation logic. The depth of these changes improved both developer experience and the reliability of federated machine learning workflows.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
19Total
Bugs
1
Commits
19
Features
4
Lines of code
4,133
Activity Months1
Your Network
38 people
Work History
September 2025
19 Commits • 4 Features
Sep 1, 2025September 2025 delivered a refreshed federated learning starter kit in adap/flower, modernized FlowerTune examples, and strengthened documentation and reliability. Key features include XGBoost Federated Quick-Start Templates aligned with the latest Flower API and FedXgbBagging integration; modernization of FlowerTune-LLM/ViT examples; comprehensive general template/tabular/baseline updates; enhanced upgrade guides and tutorials; and robustness fixes to FedAvg evaluation logic to gracefully handle zero-fraction training/evaluation.
19 Commits • 4 Features
Sep 1, 2025September 2025 delivered a refreshed federated learning starter kit in adap/flower, modernized FlowerTune examples, and strengthened documentation and reliability. Key features include XGBoost Federated Quick-Start Templates aligned with the latest Flower API and FedXgbBagging integration; modernization of FlowerTune-LLM/ViT examples; comprehensive general template/tabular/baseline updates; enhanced upgrade guides and tutorials; and robustness fixes to FedAvg evaluation logic to gracefully handle zero-fraction training/evaluation.
September 2025
Activity
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Quality Metrics
Correctness91.6%
Maintainability91.6%
Architecture91.6%
Performance83.2%
AI Usage21.0%
Skills & Technologies
Programming Languages
MarkdownPythonRSTShellTOMLreStructuredTextrst
Technical Skills
API DesignAPI RefactoringBackend DevelopmentCode ClarityCode RefactoringConfiguration ManagementDeep LearningDocumentationFederated LearningFramework DevelopmentMachine LearningModel CheckpointingPEFTPyTorchPython
Repositories Contributed To
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