FLock.io has been spotlighted by the World Economic Forum (WEF)’s MINDS programme for its privacy-preserving AI training for the NHS. It showcases use cases in eye disease detection and diabetes management that keep patient data secure. Moorfields Eye Hospital and UCLH are using FLock.io’s federated learning platform to train clinical AI models while maintaining 100% data sovereignty.
The spotlight places FLock.io’s work within the wider MINDS programme, alongside a broader ecosystem focused on scaling high-impact, real-world AI applications in collaboration with Accenture. The latest MINDS cohort includes organisations such as Lenovo, Occidental, TCL Industries, Hisense Hitachi and KUKA.
FLock.io is working with the NHS on two use cases
FLock.io is working with NHS researchers from UCL and clinical partners from University College London Hospitals (UCLH) for glucose monitoring alerts. This empowers clinicians with AI-powered predictions locally trained on 400+ patients’ data. It enables collaborative training across partners in the UK, Europe, US, and China while ensuring patient data never leaves the secure NHS trust network, maintaining 100% data sovereignty.
Approximately 14,000 end users, including patients using diabetes management apps, engage with FLock.io’s platform across the UK, Southeast Asia and East Asia. The next phase – a multi-continental glucose prediction real-world trial with 100 patients – will begin this summer.
FLock.io estimates that AI-driven prevention in the NHS could result in over £100M in annual savings, based on a 1% reduction in the £10B+ currently spent on diabetes management.
With Moorfields Eye Hospital, FLock.io has completed the initial research for federated eye disease detection. Training of the AI model using the hospital’s image data is underway. It aims to solve scaling issues that traditional centralised AI could not, and allows for multi-site training across NHS trusts without requiring them to share sensitive imaging data externally.
The long-term goal is to replicate these models across additional NHS trusts. The NHS’ single-payer system and consistent data governance make it ideal for proving federated learning at scale before expanding to other markets.
The problem FLock.io aims to solve
Data privacy regulations and security concerns restrict the use of AI by regulated industries holding sensitive data, including hospitals, banks and governmental agencies. It forces organisations to either forgo AI adoption or rely on generic models that lack domain accuracy or introduce compliance risk.
Conventional approaches – such as centralised cloud-based AI training and on-premises model deployment – typically require significant computational resources. They cannot guarantee robust privacy protection or protection against model poisoning attacks and data leaks, and can compromise model accuracy.
FLock.io’s solution allows collaborative AI model training without sharing raw data. Each participant trains the model locally and securely on-premises or on edge devices. They share only encrypted model updates, which are then aggregated to improve the model’s performance, enabling real-time inference.
More about FLock.io
FLock.io is an AI research and infrastructure company pioneering enterprise-grade federated learning and distributed AI solutions that prioritise data privacy. Its decentralised federated learning architecture and production-ready platforms (AI Arena, FL Alliance, and FLock API Platform) enable organisations to train and deploy their own custom AI models on local hardware while maintaining full data privacy, model ownership, and regulatory alignment by design.
FLock.io effectively combines FL and blockchain-based verification for a 37% improvement in model accuracy, a 44% reduction in total cost ownership, a reduced risk of data breaches or model poisoning attacks and a 63% shorter deployment time. It is more sustainable, with 80% less training energy per model update.
The government of Sarawak, Malaysia is also currently completing a sovereign AI pilot with FLock.io, including in healthcare. It will subsequently be deployed by hospital partners in the US, Europe and China and establish a standard for cross-border healthcare AI collaboration in the Asia-Pacific and Europe.
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