Ovanthra Core v2.4: Federated Intelligence

In traditional enterprise AI deployments, data silos pose a fundamental challenge. Regulatory requirements, privacy concerns, and organizational politics prevent the consolidation of data necessary for training powerful models. Federated learning solves this by bringing the model to the data, rather than the reverse.

How It Works

Instead of centralizing data, each department trains a local copy of the model on their own data. Only model updates—encrypted gradients and parameters—are shared with a central coordinator. This coordinator aggregates the updates to improve the global model, which is then distributed back to participants for the next training round.

The result: a model that learns from diverse, distributed datasets while raw data never leaves its originating department.

Key Features

• check_circleDifferential Privacy: Built-in noise injection protects individual data points from reconstruction attacks.
• check_circleSecure Aggregation: Encrypted gradient sharing ensures no single party can inspect another's training updates.
• check_circleFault Tolerance: Training continues even when individual nodes drop offline or experience failures.

This release represents a fundamental shift in how enterprises can leverage AI while respecting data governance requirements. We're seeing organizations use federated learning to build models across geographies, business units, and even partner networks.