Privacy-Preserving Federated Learning – Future Collaboration and Continued Research
Summary
This NIST Cybersecurity Insights blog post concludes a series on privacy-preserving federated learning, a collaboration between NIST and the UK government's Responsible Technology Adoption Unit (RTA). The series explores reflections and learnings from the first US-UK collaboration working with Privacy Enhancing Technologies (PETs). Readers are directed to NIST's Privacy Engineering Collaboration Space or RTA's blog for more information and previous posts.
IFF Assessment
Privacy-preserving federated learning is a beneficial technology for defenders as it enables collaborative model training without exposing sensitive data.
Severity
Defender Context
Privacy-preserving federated learning is an important area for defenders as it allows for the development of more secure and privacy-respecting AI models. Defenders should monitor the development and deployment of these technologies to ensure they are implemented securely and effectively. The rise of privacy regulations and the increasing importance of data security are driving the adoption of federated learning and other PETs.