At the core of the solution is federated learning, a technique that allows machine learning models to be trained directly on distributed edge devices without transferring raw data. This makes it possible to build accurate and scalable AI systems that comply with data privacy laws such as GDPR, HIPAA, or local sovereign frameworks. Instead of uploading data to the cloud, model updates are shared and aggregated securely via Savitri’s energy-efficient Proof of Unity consensus — a new mechanism that verifies training rounds across the network without requiring wasteful computation.

• • Model weights are updated locally and aggregated globally via Proof of Unity consensus.
  • Optimized for low-bandwidth and intermittent connectivity environments.
  • Supports horizontal and vertical federated learning architectures.