RhinoHealth/user-resources
Feb 2025 – Jun 2025
3 Months active
Languages Used
CSVDockerfileJupyter NotebookMarkdownPythonConfigurationJSONShell
Technical Skills
Data AnalysisData EngineeringData PreparationData ScienceDockerDocumentation
samholo
PROFILE
Samholo
Sam developed core federated learning resources and privacy-preserving machine learning workflows in the RhinoHealth/user-resources repository. He integrated XGBoost with NVFlare, enabling secure distributed training using Fully Homomorphic Encryption, and implemented Dockerized environments for reproducible deployment. Sam reorganized repository structures, improved documentation, and created data preparation pipelines and synthetic datasets to streamline onboarding and experimentation. His work included C++ and Python development, CMake build system management, and dependency pinning to ensure maintainability. By addressing data path bugs and enhancing code organization, Sam established a robust foundation for scalable, privacy-focused analytics, reducing setup time and supporting enterprise-ready federated learning solutions.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
18Total
Bugs
2
Commits
18
Features
4
Lines of code
110,525
Activity Months3
Your Network
13 people
Work History
June 2025
6 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for RhinoHealth/user-resources: Delivered core XGBoost with Fully Homomorphic Encryption (FHE) integration on NVFlare, enabling secure, privacy-preserving distributed training. Implemented Dockerized environment, federated learning configuration, and encryption plugin support, with a reorganized plugin directory and pinned dependencies to improve reproducibility and maintainability. No critical defects observed; minor cleanup and hardening performed during integration. Early results indicate a foundation for scalable, privacy-preserving analytics with enterprise-ready security controls.
6 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for RhinoHealth/user-resources: Delivered core XGBoost with Fully Homomorphic Encryption (FHE) integration on NVFlare, enabling secure, privacy-preserving distributed training. Implemented Dockerized environment, federated learning configuration, and encryption plugin support, with a reorganized plugin directory and pinned dependencies to improve reproducibility and maintainability. No critical defects observed; minor cleanup and hardening performed during integration. Early results indicate a foundation for scalable, privacy-preserving analytics with enterprise-ready security controls.
June 2025
March 2025
8 Commits • 1 Features
Mar 1, 2025Month: 2025-03 Key features delivered: - Federated XGBoost Training Framework and Tutorials Enhancements in RhinoHealth/user-resources: introduced federated training setup with data preparation scripts and client/server configurations; reorganized repository directories (xgboost to xgboost-horizontal); cleaned deprecated local training examples; updated deployment README/docs and reorganized advanced tutorials. Major bugs fixed: - Fixed initialization order of data_dirs and TRAIN_DATA_PATH in XGBoost Flare to prevent runtime errors related to data path configuration. Overall impact and accomplishments: - Enabled scalable, privacy-preserving federated ML workflows with a clearer project structure and up-to-date tutorials, reducing onboarding time and deployment risk. Improvements to docs and organization also set the stage for smoother production readiness and extended contributor collaboration. Technologies/skills demonstrated: - XGBoost, federated learning concepts, Python scripting and automation, repository refactoring, documentation, client/server configuration, data pipelines, debugging and code hygiene.
March 2025
8 Commits • 1 Features
Mar 1, 2025Month: 2025-03 Key features delivered: - Federated XGBoost Training Framework and Tutorials Enhancements in RhinoHealth/user-resources: introduced federated training setup with data preparation scripts and client/server configurations; reorganized repository directories (xgboost to xgboost-horizontal); cleaned deprecated local training examples; updated deployment README/docs and reorganized advanced tutorials. Major bugs fixed: - Fixed initialization order of data_dirs and TRAIN_DATA_PATH in XGBoost Flare to prevent runtime errors related to data path configuration. Overall impact and accomplishments: - Enabled scalable, privacy-preserving federated ML workflows with a clearer project structure and up-to-date tutorials, reducing onboarding time and deployment risk. Improvements to docs and organization also set the stage for smoother production readiness and extended contributor collaboration. Technologies/skills demonstrated: - XGBoost, federated learning concepts, Python scripting and automation, repository refactoring, documentation, client/server configuration, data pipelines, debugging and code hygiene.
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for RhinoHealth/user-resources: Delivered end-to-end Federated Learning resources for the XGBoost NVFlare 2.4 Client API, created practical data assets for testing, and corrected documentation to improve onboarding. Key features delivered include the Federated Learning Setup Guide (setup, data prep, local model development, federated training adaptation, Docker setup, runner/analysis), Demo and testing data resources (synthetic data A.csv, B.csv, test.csv, and credit risk dataset) with supporting scripts, and README fix to reference the correct inference script (infer.py). Major bugs fixed: README now points to infer.py instead of predict.py. Overall impact: accelerates federated experimentation, reduces setup time, improves reproducibility and onboarding, enabling faster proof-of-concept cycles and broader adoption of federated capabilities. Technologies/skills demonstrated: XGBoost client API, NVFlare 2.4 Client API, federated learning, Docker, data preparation, synthetic data assets, and documentation quality.
4 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for RhinoHealth/user-resources: Delivered end-to-end Federated Learning resources for the XGBoost NVFlare 2.4 Client API, created practical data assets for testing, and corrected documentation to improve onboarding. Key features delivered include the Federated Learning Setup Guide (setup, data prep, local model development, federated training adaptation, Docker setup, runner/analysis), Demo and testing data resources (synthetic data A.csv, B.csv, test.csv, and credit risk dataset) with supporting scripts, and README fix to reference the correct inference script (infer.py). Major bugs fixed: README now points to infer.py instead of predict.py. Overall impact: accelerates federated experimentation, reduces setup time, improves reproducibility and onboarding, enabling faster proof-of-concept cycles and broader adoption of federated capabilities. Technologies/skills demonstrated: XGBoost client API, NVFlare 2.4 Client API, federated learning, Docker, data preparation, synthetic data assets, and documentation quality.
February 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture85.6%
Performance85.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeCSVConfigurationDockerfileJSONJupyter NotebookMarkdownPythonShell
Technical Skills
Build System ConfigurationBuild System ManagementC++ DevelopmentCMake Build SystemCode CleanupCode OrganizationData AnalysisData EngineeringData PreparationData PreprocessingData ScienceDependency ManagementDockerDocumentationFederated Learning
Repositories Contributed To
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