Public Funding for Raiven Limited

Data privacy and security is a key concern for the adoption of Artificial Intelligence/Machine Learning with IoT (Internet of Things), particularly where that data is personal or security sensitive. The number of IoT-enabled devices and machines is growing exponentially, estimated to reach 26Bn globally by 2030\. Creating platforms optimised to maximise private and secure machine learning at the edge in distributed systems that can be trusted is an urgent priority. The consortium's mission is to help solve the challenge of implementing trustworthy AI-in-IoT. This will be achieved by accelerating the development of a federated, secure, privacy-preserving, and auditable AI-for-IoT platform optimised for machine learning in IoT and edge systems. OctaiPipe is a first-of-its-kind innovation that combines privacy-preserving machine learning technology, cyber security, continuous collaborative learning and AI lifecycle management. This will allow IoT-enabled businesses to build, deploy, and manage machine learning software that guarantees the privacy and security of device data and its use, allowing the user to have a high degree of trust in the AI solutions embedded in them. Many organisations already collect high-level operational and HSE incident data intelligence through various Industrial IoT devices and cameras to successfully predict safety incidents. However, analytics based on this is not meaningfully actionable to drive changes that preventatively reduce risks. For predictions to be meaningfully actionable, they must be made at a sufficient level of granularity within the workgroup. Technology now exists to monitor such events at granularity---enabling the build of models to predict and forecast events in the future at sufficient granularity---enabling game-changing preventative impact at the workgroup level. Fortunately, HSE critical events are rare within single sites or even organisations---meaning insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented. However, progress towards an AI-enabled solution is impeded by: a) a requirement for more observations than one organisation can generate alone, so it is imperative to share data, and b) barriers to sharing data across organisations that, until now, have not been overcome. Federated Learning solves this. The project will enable organisations to combine data to facilitate actionable incident predictions for small work groups. This project aims to address vulnerabilities in Machine Learning for IoT with a specific focus on FL and addresses the fundamental challenges of socially responsible AI adoption into society.