Federated Learning

In the context of the Digital Personal Data Protection Act, 2023 (DPDP Act), federated learning refers to an approach where artificial intelligence models are trained across multiple systems or locations while keeping the underlying personal data within its original environment. Instead of transferring raw data to a central location, organizations share model updates or insights that allow the model to improve while reducing direct movement of personal data.

Organizations increasingly use artificial intelligence and machine learning for activities such as fraud detection, personalization, healthcare analytics, customer service automation, and risk assessment. Traditional machine learning approaches often require large datasets to be combined in centralized environments, which can increase privacy and security risks. Federated learning can help reduce these risks by limiting unnecessary data transfers while enabling collaborative model development across different data environments.

The DPDP Act does not specifically mention or require federated learning. However, the approach may support privacy-conscious data processing by helping organizations apply principles such as minimizing unnecessary data movement and reducing exposure of personal data. Organizations using federated learning must still comply with applicable DPDP obligations, including ensuring lawful processing, providing appropriate notices, maintaining security safeguards, and protecting Data Principal rights.

In practice, gaps emerge when:

• Organizations centralize large volumes of personal data unnecessarily for AI model training.
• AI teams lack visibility into what personal data is being used for model development.
• Model training processes do not have clear ownership or governance oversight.
• Organizations assume federated learning automatically eliminates privacy risks.
• Data flows, model updates, and processing purposes are not properly documented.

Organizations address these challenges by implementing AI governance practices, maintaining visibility into data used for machine learning, documenting processing activities, applying privacy assessments, and evaluating privacy-enhancing technologies where appropriate. Within Privy, capabilities such as data discovery, data classification, data mapping, privacy assessments, governance workflows, and audit-ready reporting help organizations understand and govern personal data used across AI-enabled environments.