Federated Learning Deployment Guide

Note

This guide is a practical companion to the PDPC Guide to Federated Learning, jointly developed by the Personal Data Protection Commission (PDPC) and GovTech. It builds on that foundation by showing how FL concepts can be translated into a practical cross-silo deployment pattern.

Federated learning (FL) enables multiple parties to train a shared machine-learning model without pooling raw data in one place. This can be useful where data collaboration is valuable, but centralisation is constrained by policy, privacy, security, operational, or commercial considerations.

This guide provides a practical starting point for cross-silo FL deployment using an open-source reference stack (Flower, PyTorch, Opacus), developed and tested in GovTech’s Government Commercial Cloud (GCC) environment (AWS-based). The architectural patterns and operational practices are intended to be broadly applicable across different cloud environments. The reference implementation should be adapted based on the use case, trust model, data sensitivity, and governance requirements.

The guide uses a progressive structure: start with a local simulation, understand the reference architecture, prepare for a governed cloud pilot, then decide what additional production hardening is needed. It does not replace the deploying organisation’s required approvals, legal assessment, security review, or cloud governance standards.

Reader’s guide

Reader	How to use this guide
Project sponsors and product owners	Sections 1–4 and 10 for use-case fit, commitments, approvals, and costs.
Data scientists and model owners	Sections 3, 6, and 7 for FL workflow, data readiness, baselines, and model release.
Data engineers and platform teams	Sections 2–5, 7, and 9–11 for architecture, deployment, training, operations, and troubleshooting.
Security and privacy teams	Sections 5, 7–10 for authentication, secure aggregation, DP, logging, and incident response.
Governance and compliance teams	Sections 4, 6, 7, and 10 for decisions and evidence before moving to production.

A glossary of terms used in this guide is provided in Appendix H.

Mapping to the PDPC Guide to Federated Learning

View mapping table

Concept	Where it appears in this deployment guide
FL suitability and alternatives	Section 1 and Section 2.1 explain that readers should first assess suitability using the PDPC Guide to Federated Learning before using this deployment guide.
Readiness checklist	Section 4.2 covers minimum readiness before a cloud pilot; Section 4.9 provides a suggested pilot plan.
Design option register	Sections 2, 4, 5, and 7 translate design choices into reference architecture, deployment pattern, privacy controls, and training configuration.
FL-specific risks	Section 5 covers the starter threat model, authentication, secure aggregation, differential privacy, supply-chain security, and audit logging.
Technical controls	Sections 5, 7, 8, 9, and the operational appendices provide implementation-oriented control guidance.
Cost considerations	Section 10.3 and Appendix F covers cost drivers and sponsor sign-off; Appendix F provides an indicative reference estimate.
Data heterogeneity	Section 6.4 covers heterogeneity checks, practical responses, and per-participant evaluation.
Quick-start resources	Section 3 covers local simulation; Section 2.4 and Appendix B explain the repository and implementation artefacts.

Chapters

2. Reference Deployment at a Glance Reference assumptions, deployment components, and repository structure.

3. Quickstart: Run a Local FL Simulation Set up and run a local FL simulation using synthetic data.

4. From Simulation to Cross-Silo Cloud Deployment Implementation path, readiness checklist, roles, deployment sequence, and pilot plan.

5. Privacy and Security Controls Threat model, mTLS, secure aggregation, differential privacy, and audit logging.

6. Data and Model Readiness Data requirements, manifest validation, baselines, heterogeneity, and evidence capture.

7. Training, Evaluation, and Model Management Task configuration, training workflow, privacy testing, fairness, and model registry.

8. Optional Extension: Secure Entity Alignment for Vertical FL PSI-based entity alignment for vertical federated learning use cases.

9. Operations, Monitoring, Backup, and Recovery Observability stack, dashboards, alarms, retraining, backup, and disaster recovery.

10. Governance, Approval, and Cost Considerations Governance checkpoints, federation agreement, cost drivers, and production readiness.

11. Troubleshooting Common Issues Diagnostic approach and troubleshooting table for common FL deployment issues.

Appendices Framework alternatives, repository checklist, environment configuration, PKI, secure inference, cost estimates, incident response, glossary, and operator checklist.