securefederatedai/openfl
Feb 2025 – May 2025
3 Months active
Languages Used
Jupyter NotebookPythonYAML
Technical Skills
Data ScienceFederated LearningImage SegmentationMachine LearningPyTorchWorkflow API
Efrat1
PROFILE
Efrat1
Efrat Dar developed and enhanced federated learning workflows in the securefederatedai/openfl repository, focusing on robust data management and cloud storage integration. She refactored the UNet PyTorch tutorial to leverage the OpenFL Workflow API, enabling reproducible image segmentation experiments with federated training. Efrat introduced verifiable dataset features with PyTorch data loading, hashing algorithms, and validation to ensure data integrity across distributed environments. She expanded data source support to include AWS S3 and Azure Blob Storage, implementing file enumeration and provenance tracking. Her work, primarily in Python and Jupyter Notebook, improved the reliability, scalability, and auditability of federated learning pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
3,583
Activity Months3
Your Network
1991 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025Month 2025-05: Delivered broadened data source capabilities and strengthened dataset verification in securefederatedai/openfl, enabling Azure Blob Storage as a first-class data source alongside existing local and S3 sources. Implemented Azure data access with file enumeration, hashing, and blob reading; integrated with VerifiableDatasetInfo to ensure end-to-end provenance for Azure-stored datasets. Created an example PyTorch histology workflow leveraging S3 storage to illustrate end-to-end reproducible experiments. Added a dataset hashing command to streamline verification and integrity checks across datasets. Included experiment integration updates for Edar/datasets to improve cross-source experimentation workflows.
2 Commits • 1 Features
May 1, 2025Month 2025-05: Delivered broadened data source capabilities and strengthened dataset verification in securefederatedai/openfl, enabling Azure Blob Storage as a first-class data source alongside existing local and S3 sources. Implemented Azure data access with file enumeration, hashing, and blob reading; integrated with VerifiableDatasetInfo to ensure end-to-end provenance for Azure-stored datasets. Created an example PyTorch histology workflow leveraging S3 storage to illustrate end-to-end reproducible experiments. Added a dataset hashing command to streamline verification and integrity checks across datasets. Included experiment integration updates for Edar/datasets to improve cross-source experimentation workflows.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025: Focused on strengthening data integrity, source versatility, and governance for secure federated learning in openfl. Delivered verifiable datasets with PyTorch loading/verification and robust hashing/validation, enabling reproducible and auditable FL runs. Added S3 data source support via a new S3DataSource class integrated into the data source framework, accompanied by targeted data source management fixes to improve reliability and compliance. No standalone bug-fix tickets were logged this month; the work was primarily feature-driven with cross-repo integration.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025: Focused on strengthening data integrity, source versatility, and governance for secure federated learning in openfl. Delivered verifiable datasets with PyTorch loading/verification and robust hashing/validation, enabling reproducible and auditable FL runs. Added S3 data source support via a new S3DataSource class integrated into the data source framework, accompanied by targeted data source management fixes to improve reliability and compliance. No standalone bug-fix tickets were logged this month; the work was primarily feature-driven with cross-repo integration.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 — Key accomplishment: Refactored the UNET PyTorch tutorial to the OpenFL Workflow API, adding a new Jupyter notebook that defines a complete federated learning workflow for image segmentation using UNet. Includes data loading, model definition, participant orchestration, and end-to-end federated training leveraging the OpenFL workflow. This work enhances reproducibility and scalability for federated experiments in securefederatedai/openfl.
1 Commits • 1 Features
Feb 1, 2025February 2025 — Key accomplishment: Refactored the UNET PyTorch tutorial to the OpenFL Workflow API, adding a new Jupyter notebook that defines a complete federated learning workflow for image segmentation using UNet. Includes data loading, model definition, participant orchestration, and end-to-end federated training leveraging the OpenFL workflow. This work enhances reproducibility and scalability for federated experiments in securefederatedai/openfl.
February 2025
Activity
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Quality Metrics
Correctness94.0%
Maintainability92.0%
Architecture94.0%
Performance76.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonYAML
Technical Skills
API IntegrationAWS S3Backend DevelopmentCloud Storage IntegrationConfiguration ManagementData IntegrityData LoadingData ManagementData ScienceData Source ManagementData VerificationDataset ManagementFederated LearningFederated Learning Data HandlingHashing Algorithms
Repositories Contributed To
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