Privacy-first ear biometric segmentation - 99%+ accuracy with <2M parameters for edge authentication and GDPR compliance

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

Production Android AI with ExecuTorch 1.0 - Deploy PyTorch models to mobile with NPU acceleration and 50KB footprint

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.

Secure, local-first workbench for refining LLM prompts. Features PII sanitization, Shadow Model architecture, and BIP39 encryption. No data leaves your machine.

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

A decentralized, diffusion-based U-Net framework for privacy-preserving brain tumor segmentation from MRI images.

A Modular Knowledge Transfer System for Large Language Models

🤝 Enable federated AI and compute sharing while preserving privacy, empowering users to control their data and collaborate on decentralized models.

A privacy-preserving Federated Learning implementation on CIFAR-10 using PyTorch and PySyft. Simulates distributed training across 10 virtual workers with secure model aggregation.

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