securefederatedai/openfl
May 2025 – May 2025
1 Month active
Languages Used
MarkdownPythonShellYAML
Technical Skills
Distributed SystemsFederated LearningJupyter NotebooksMachine LearningPython
Tayfun
PROFILE
Tayfun
Developed and integrated the CrowdGuard Federated Runtime within the securefederatedai/openfl repository, enabling a fully distributed federated learning environment. This work transitioned the system from local simulations to scalable, production-like deployments by introducing new configuration files in YAML, Python scripts for managing envoy attributes, and a Jupyter notebook to orchestrate end-to-end federated experiments. Leveraging expertise in distributed systems and machine learning, the implementation laid the groundwork for secure, multi-party analytics. The approach focused on modularity and reproducibility, allowing for seamless collaboration across nodes and accelerating time-to-insight for federated analytics use cases without addressing bug fixes during this period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,813
Activity Months1
Your Network
164 peopleWork History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Delivered CrowdGuard Federated Runtime Integration with OpenFL, enabling a fully distributed federated setup. Implemented new configuration files, Python scripts for envoy attributes, and a Jupyter notebook to orchestrate federated experiments, moving beyond local simulations toward scalable production-like runs. This work, tracked under commit df4a74ec8de64f6fdec345c4a6a7290124caa0bc and related to CrowdGuard example (#1650), lays the foundation for secure, distributed federated analytics and accelerates time-to-insight for multi-party collaboration.
1 Commits • 1 Features
May 1, 2025May 2025: Delivered CrowdGuard Federated Runtime Integration with OpenFL, enabling a fully distributed federated setup. Implemented new configuration files, Python scripts for envoy attributes, and a Jupyter notebook to orchestrate federated experiments, moving beyond local simulations toward scalable production-like runs. This work, tracked under commit df4a74ec8de64f6fdec345c4a6a7290124caa0bc and related to CrowdGuard example (#1650), lays the foundation for secure, distributed federated analytics and accelerates time-to-insight for multi-party collaboration.
May 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonShellYAML
Technical Skills
Distributed SystemsFederated LearningJupyter NotebooksMachine LearningPython
Repositories Contributed To
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