December 2024 (VectorInstitute/FL4Health): Delivered a robust fix to Masked Model Conversion by implementing recursive masking across all submodules, ensuring all layers (linear, convolutional, normalization, including transposed convolutions) are masked. This eliminates partial masking and increases robustness of the masking workflow. The fix targeted the convert_to_masked_model path and was implemented through a bug fix on convert_to_masked_layers (commit 21997fdc4e3611fd6829fce53a71bd757d5aedf6). Impact: more reliable masking, safer deployment, and improved reproducibility across environments. Technologies/skills demonstrated: Python, PyTorch, recursive traversal, code refactoring, testing. Business value: reduces masking-related issues in production, enabling faster and safer rollout of masked models.