securefederatedai/openfl
Nov 2024 – Jan 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data ScienceFederated LearningMachine LearningOpenFLPython
Kush Agrawal
PROFILE
Kush Agrawal
Kush Agrawal developed and refined federated learning resources within the securefederatedai/openfl repository, focusing on practical onboarding and workflow clarity. He created an end-to-end tutorial for linear regression using OpenFL, demonstrating model definition, synthetic data generation, federated training, and evaluation in a reproducible Jupyter Notebook environment. In a subsequent update, Kush streamlined the NumPy-based linear regression workflow, removing unnecessary output redirection and simplifying collaborator logic to improve reliability and reduce onboarding friction. His work leveraged Python, NumPy, and OpenFL, delivering well-structured, maintainable code that enhanced both the usability and clarity of federated learning samples for new contributors.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
581
Activity Months2
Your Network
1991 people
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for securefederatedai/openfl. Focused on streamlining the NumPy linear regression workflow sample within the OpenFL framework to improve reliability, clarity, and onboarding for contributors. Key changes reduced noise from stdout, simplified collaborator iteration logic, and clarified execution flow, with planned exclusion of 'private' collaborators in the next step.
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for securefederatedai/openfl. Focused on streamlining the NumPy linear regression workflow sample within the OpenFL framework to improve reliability, clarity, and onboarding for contributors. Key changes reduced noise from stdout, simplified collaborator iteration logic, and clarified execution flow, with planned exclusion of 'private' collaborators in the next step.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for securefederatedai/openfl: Delivered a new OpenFL Federated Learning Tutorial for Linear Regression, enabling end-to-end demonstration of model definition, synthetic data generation, federated training workflow, and evaluation. This work strengthens onboarding, showcases practical federated learning patterns, and adds tangible developer value.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for securefederatedai/openfl: Delivered a new OpenFL Federated Learning Tutorial for Linear Regression, enabling end-to-end demonstration of model definition, synthetic data generation, federated training workflow, and evaluation. This work strengthens onboarding, showcases practical federated learning patterns, and adds tangible developer value.
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data ScienceFederated LearningMachine LearningOpenFLPython
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