Federated Analytics Guide

Note

This guide is a deep dive into the Federated Analytics chapter of the PDPC Guide to Federated Learning, jointly developed by the Personal Data Protection Commission (PDPC) and GovTech. It builds on the concepts introduced there and provides more detailed guidance on FA architecture, governance and operating models, risk management, and implementation considerations.

Federated Analytics (FA) enables organisations to perform distributed statistical analysis across multiple data sources. Analytical queries are executed locally at each participant, and only aggregated results are shared. This allows organisations to generate insights such as counts, averages, trends, and correlations without centralising raw data.

1. What is Federated Analytics? An overview of how FA works, its core query-based workflow, and the types of analytics it supports.

2. Architecture Key system components and the centralised vs decentralised architectural patterns.

3. Governance and Operating Model Roles, responsibilities, and governance considerations for running an FA deployment.

4. Risk Management Key risk concerns in FA deployments and how Privacy-Enhancing Technologies can help.

5. Implementation Considerations Data harmonisation, statistical validity, and operational considerations for real-world deployments.

6. Case Studies Cross-border and system-wide examples of FA in practice.

7. Getting Started Reading materials, open-source tools, and resources for practitioners.