APPFL/APPFL
Oct 2025 – Oct 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
Deep LearningDifferential PrivacyMachine LearningNeural NetworksPyTorchPython
minseok-ryu
PROFILE
Minseok-ryu
Minseok Ryu developed an end-to-end differential privacy integration for the APPFL/APPFL repository, focusing on privacy budget management and the transition from a legacy mechanism to an Opacus-based solution. He refactored the training pipeline to attach the privacy model and compute the accumulated privacy budget, simplifying gradient clipping by leveraging the clip_grad configuration. His work included introducing differential privacy-compatible activations in convolutional neural networks and updating the MNIST example to demonstrate the new workflow with an adjusted client count. Utilizing Python, PyTorch, and deep learning techniques, Minseok delivered a cohesive, production-ready privacy solution with thoughtful algorithmic improvements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
39
Activity Months1
Your Network
14 people
Work History
October 2025
4 Commits • 1 Features
Oct 1, 2025Month 2025-10: Delivered end-to-end differential privacy integration for APPFL/APPFL using Opacus, including privacy budget management, removal of legacy DP mechanism, and a DP-enabled MNIST example. Refactors simplified gradient clipping to rely on clip_grad configuration, introduced DP-compatible activations, and aligned the training pipeline to attach the privacy model to compute the accumulated privacy budget. Key commits include four updates to CNN DP compatibility, gradient clipping refactor, and MNIST example enhancement: adb1fcc5d103df214b0947a176957280a4d3ce85; a78b8c6c0d1bacf15d0fc383e0ae7e7bac021dd6; a5fb85fa5ad50003ec7ab7338f1a3180b36bc63b; ce75cbce6beb67fb6587ca9cc90c7b6870cb561b.
4 Commits • 1 Features
Oct 1, 2025Month 2025-10: Delivered end-to-end differential privacy integration for APPFL/APPFL using Opacus, including privacy budget management, removal of legacy DP mechanism, and a DP-enabled MNIST example. Refactors simplified gradient clipping to rely on clip_grad configuration, introduced DP-compatible activations, and aligned the training pipeline to attach the privacy model to compute the accumulated privacy budget. Key commits include four updates to CNN DP compatibility, gradient clipping refactor, and MNIST example enhancement: adb1fcc5d103df214b0947a176957280a4d3ce85; a78b8c6c0d1bacf15d0fc383e0ae7e7bac021dd6; a5fb85fa5ad50003ec7ab7338f1a3180b36bc63b; ce75cbce6beb67fb6587ca9cc90c7b6870cb561b.
October 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture85.0%
Performance80.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Deep LearningDifferential PrivacyMachine LearningNeural NetworksPyTorchPythonPython scriptingalgorithm developmentdata privacymachine learning
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