securefederatedai/openfl
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPythonYAML
Technical Skills
Data ScienceFederated LearningMachine LearningOpenFLPyTorchAPI Integration
Ynon Flum
PROFILE
Ynon Flum
Ynon Flum developed two end-to-end federated learning workflows for the securefederatedai/openfl repository, focusing on practical onboarding and privacy-preserving model aggregation. He created a PyTorch-based tutorial demonstrating the FedProx algorithm on MNIST, covering model definition, data loading, participant setup, and federated training execution. In a subsequent release, he implemented a FedCurv-enabled example using the TaskRunner API, introducing a CNN model and a custom dataloader for histology data, along with configuration updates for FedCurv aggregation. His work leveraged Python, PyTorch, and YAML, providing reproducible, production-ready examples that improved repository structure and accelerated adoption for federated learning practitioners.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,067
Activity Months2
Your Network
1991 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered a new FedCurv-enabled federated learning example for OpenFL TaskRunner, introducing a PyTorch CNN model, a Histology dataset dataloader, and configuration updates to enable FedCurv aggregation. The release provides an end-to-end, reproducible workflow that demonstrates privacy-preserving model aggregation in a production-ready TaskRunner setup, facilitating faster adoption by developers and teams experimenting with FedCurv in OpenFL.
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered a new FedCurv-enabled federated learning example for OpenFL TaskRunner, introducing a PyTorch CNN model, a Histology dataset dataloader, and configuration updates to enable FedCurv aggregation. The release provides an end-to-end, reproducible workflow that demonstrates privacy-preserving model aggregation in a production-ready TaskRunner setup, facilitating faster adoption by developers and teams experimenting with FedCurv in OpenFL.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly performance summary for securefederatedai/openfl: Delivered a new Federated Learning Tutorial using PyTorch and the OpenFL FedProx workflow on MNIST. The end-to-end tutorial covers model definition, data loading, participant setup, and execution of a federated training flow, providing a practical onboarding resource and a concrete example of FedProx in action. Commit 918e1253aaeb681b221c728a055192a0d3440dc4 ("Add pytorch MNIST Workflow tutorial (#1158)") anchors the delivery. No major bugs fixed this month in the provided data. Impact: accelerates onboarding for federated learning workflows, showcases FedProx integration, and strengthens the repository's value for developers and enterprises.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly performance summary for securefederatedai/openfl: Delivered a new Federated Learning Tutorial using PyTorch and the OpenFL FedProx workflow on MNIST. The end-to-end tutorial covers model definition, data loading, participant setup, and execution of a federated training flow, providing a practical onboarding resource and a concrete example of FedProx in action. Commit 918e1253aaeb681b221c728a055192a0d3440dc4 ("Add pytorch MNIST Workflow tutorial (#1158)") anchors the delivery. No major bugs fixed this month in the provided data. Impact: accelerates onboarding for federated learning workflows, showcases FedProx integration, and strengthens the repository's value for developers and enterprises.
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonYAML
Technical Skills
API IntegrationCNNData LoadingData ScienceFederated LearningMachine LearningOpenFLPyTorch
Repositories Contributed To
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