Train machine learning models together without ever sharing your private data.

When you train an AI, it usually needs access to data. But what if your data is private—like hospital patient records, financial transactions, or personal AI assistants? With Federity, your data stays on your device.

Instead of sending your data to a server, Federity sends the model to you. It learns from your data privately and only sends back small updates (like improved settings). These updates are combined with others to improve the global AI model without ever exposing anyone’s data.

Every contribution is verifiable and stored as an NFT, proving your work and ensuring that only authorized people can access model details.

Federity is different from other collaborative ML platforms like Hugging Face because it is built for privacy. No one—not even Federity—can extract data from your models.

• ✅ Your Data Stays Private – The model trains on your device, and only updates (not your data) are shared.
• ✅ Verifiable Training – Others can verify that your training is legit without seeing any details.
• ✅ Full Ownership – Every commit you make is recorded as an NFT, proving your intellectual work.
• ✅ Access Control – Only authorized people can access model details, ensuring total security.

🛠 How It Works

• 1️⃣ Upload a model – Start by creating a new ML model repository.
• 2️⃣ Invite trusted collaborators – Just like GitHub, invite contributors to train the model.
• 3️⃣ Train Locally – Models are trained on personal devices—your data never leaves your system.
• 4️⃣ Share Updates, Not Data – Only model updates (not the actual data) are shared and merged.
• 5️⃣ Verify with Zero-Knowledge Proofs – Contributions are checked for authenticity without exposing any data.
• 6️⃣ Own Your Work – Every update is stored as an NFT, ensuring proof of contribution.

• PyTorch

• Create a new repository
• Push an untrained model
• Invite collaborators
• Contributors train the model locally
• Push updates (not data!)
• Verify & merge contributions

A Zero-Knowledge Proof (ZKP) lets a contributor prove that they’ve trained a machine learning model on their private data without actually revealing the data.

✅ Your updates are verified as legit, but no one can see your data.
✅ Prevents fraud—ensures real contributions without revealing secrets.
✅ Privacy first – Keeps all training data hidden from others.

This ensures Federity remains 100% private while still allowing global collaboration.

🔹 Train smarter. Collaborate securely. Own your work. Federity makes machine learning truly private. 🚀