Federated Learning has emerged as a promising Privacy Enhancing Technology. It is a decentralised machine learning technique where a shared artificial intelligence (“AI“) model is trained across multiple separate devices or servers without exchanging any raw local data. Instead of moving data to a central cloud, the model goes to the data, trains locally, and sends only small model updates back to a central server.
To help organisations understand and adopt Federated Learning, the Personal Data Protection Commission (“PDPC“) has launched a Guide on Federated Learning (“Guide“). The Guide includes an adoption roadmap for businesses to assess whether Federated Learning is suitable for them, understand what is needed to adopt Federated Learning and how to design and configure a suitable Federated Learning solution.
The Guide covers the following topics on Federated Learning:
- What is Federated Learning, including categorisation by Federation Types and by Coordination Architectures.
- How is Federated Learning used, including use-case archetypes and case studies.
- Key implementation challenges, including business challenges, operational challenges, and algorithmic challenges.
- Federated Learning adoption roadmap, which covers three core stages: (i) assess suitability, which establishes strategic alignment; (ii) assess readiness, which focuses on operational planning; and (iii) design and configure, which delivers the technical execution.
- Risk management recommendations, including distributed governance and legal frameworks, federated data processing, collaboration and permission controls, and technical safeguards.
The Guide also provides a high-level overview of Federated Analytics, which is the practice of applying data science methods to the analysis of raw data stored locally on the participants’ side. It introduces Federated Analytics as a common precursor and complementary capability to Federated Learning deployments, including the types of analytics supported, relevant case studies, and risk management recommendations.
The Guide is intended for business, technology and data protection stakeholders who have a foundational awareness of Privacy Enhancing Technologies and are exploring whether Federated Learning is the right solution to address their specific data sharing and collaboration challenges.
Click on the following links for more information:
- Infocomm Media Development Authority (“IMDA“) Press Release titled “Privacy Enhancing Technologies: New Federated Learning Guide and Use Cases under IMDA’s PET Sandbox” (available on the IMDA website at www.imda.gov.sg)
- Guide on Federated Learning (available on the PDPC website at www.pdpc.gov.sg)
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