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.

Privacy-first decentralized AI training network combining federated learning, blockchain incentives, and quantum-safe cryptography. Enable secure collaborative model development without sharing raw data.

Federated training on MNIST with differential privacy noise + FL metrics tracking

Agentic digital health assistant, powered by Federated Learning, autonomously supports patient recovery post-discharge while preserving privacy across clinical institutions.

A stateful AI agent framework powered by the Cognitive Lattice to solve complex tasks with persistent memory and reliable tool orchestration.

Build a decentralized AI infrastructure on Solana, enabling secure on-chain model training and creating a global marketplace for AI inference services.

A vision for an open, democratic AI infrastructure — where individuals and communities share knowledge and compute without losing autonomy or privacy.