Federated Swarm Model Training (Privacy-Preserving AI)

Traditional machine learning requires centralized data. AsunaOS inverts this paradigm through federated learning, where robots:

• train AI models locally

• extract gradients

• encrypt updates

• push only model improvements on-chain

• without exposing raw sensor data

This ensures:

• privacy protection

• decentralized intelligence evolution

• collaborative training without central servers

Over time, the global model continuously improves based on contributions from millions of robots, each exposed to real-world environments—providing natural, real-time contextual learning at planetary scale.