
Over a two-month period, contributed two end-to-end federated learning examples to the securefederatedai/openfl repository, focusing on practical onboarding and advanced aggregation techniques. Developed a comprehensive PyTorch tutorial demonstrating the FedProx workflow on MNIST, covering model definition, data loading, participant setup, and federated training execution. Subsequently, implemented a FedCurv-enabled example using the TaskRunner API, introducing a PyTorch CNN model and a Histology dataset loader, along with configuration updates for privacy-preserving aggregation. Work emphasized reproducibility, clear documentation, and production-ready workflows, leveraging Python, Jupyter Notebook, and YAML to support adoption of federated learning patterns in real-world machine learning projects.
December 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: 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.
November 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 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.

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