A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

An Open Framework for Federated Learning.

Official code for "DCT-CryptoNets: Scaling Private Inference in the Frequency Domain" [ICLR 2025]

A curated collection of privacy-preserving machine learning techniques, tools, and practical evaluations. Focuses on differential privacy, federated learning, secure computation, and synthetic data generation for implementing privacy in ML workflows.

My journey from law to code: Projects in Privacy-Preserving ML, LegalTech automation, and regulatory compliance systems.

Detection of rare child diseases by applying graph machine learning to a remote dataset with federated machine learning

A deep learning solution for brain tumor segmentation using multi-modal MRI scans, integrating U-Net models, differential privacy, adversarial training, and explainability (Grad-CAM, attention scores) for robust and trustworthy medical AI.