Introducing SAFERAI: Safety Advancing Federated Estimation of Risk using AI

6 minutes

In an era where safeguarding data privacy and security are paramount, the convergence of privacy preserving machine learning and IoT technology presents both immense opportunities and daunting challenges. The need to prioritise trustworthy AI through development that is robust, explainable and transparent is an urgent priority.

Recognising this pivotal moment, OctaiPipe, a pioneer in federated edge AI innovation, has joined forces with industry leaders P&G, SME partners Site Assist, Raiven and Digital Catapult and InnovateUK to deliver a groundbreaking SaferAI concept.

Read on or join our upcoming webinar on July 16th to learn more: 

“Bringing together SMEs, large organisations and research organisations, these novel solutions will demonstrate how trusted AI and machine learning technologies can aid and be incorporated into many of the UK’s industries and sectors.”

Dr Kedar Pandya, UKRI Technology Missions Fund Senior Responsible Owner

“Federated Learning with OctaiPipe is unlocking potential not yet realized in industry. It’s enabling competing business to collaborate on the use of technology and AI for good. Specifically, within SaferAI, I can see significant value in utilizing federated learning to share model results for predicting safety incidents and risks. I can see this changing approaches in industrial safety. Before, we could only cooperate on best practices, technology or processes, but now we can actually prevent incidents with the use of AI and shared data.”

Christoph Wagner-Gillen, P&G Product Supply HSE Governance


SaferAI, or as the projects catchier title suggests, Safety advancing federated estimation of risk using AI, is a 12-month, £1.64m project focused on the development of a highly comprehensive federated learning model for predicting incident occurrences and risk.

SaferAI is an industry first, enabling world leading consumer goods manufacturer, P&G, to collaborate with other interested companies to forecast safety incident rates to reduce Health & Safety (H&S) incidents.

Until now, with low event occurrence in any single setting, there has been insufficient data availability to predict future incident events or near-misses. However, using federated learning with OctaiPipe, multiple large-scale parties with similar Health, Safety and Environment (HSE) situations can now collaborate to pool intelligence to achieve scale.

HSE in the workplace

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 a factory or workgroup.

Whilst we’re fortunate that HSE critical events are rare within single sites or even organisations, it means insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented.

To date, progress towards an AI-enabled solution has been impeded by:

  1. a) a requirement for more observations than one organisation can generate alone, so it is imperative to share data, and
  2. b) barriers to sharing data across organisations that, until now, have not been overcome.


Federated learning solves this. SaferAI will enable organisations to combine data to facilitate actionable incident predictions for small work groups.

“The UK’s innovative approach to AI regulation has made us a world leader in both AI safety and AI development.

Michelle Donelan, Secretary of State for Science, Innovation, and Technology

Federated Learning

Federated Learning is a privacy preserving machine learning technique that allows for machine learning models to be trained across multiple distributed edge devices without the need to see or move the data.

It is a key technology in the convergence of AI, IOT and connectivity as it enables edge intelligence whilst safeguarding sensitive or private data.

Federated Learning will be used the project to establish a viable on-device (Edge) AI system that pairs privacy-focused telemetry and computer vision (CV) systems. It will also enable continual collaborative learning in edge environments, combining model results provided by collaborating organisations to yield a highly comprehensive HSE model.

During SaferAI, OctaiPipe are further developing and validating as a high-trustworthiness FL-for-IoT system along with a high-accuracy model that uses observed use case data generated by AIoT camera systems. This solves the challenge of predicting safety critical incidents, near misses and hazardous/non-compliant conditions in industrial settings, enabling organisations to pre-emptively take remedial/mitigating actions to reduce H&S risks.

The consortium

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.

Raiven will be building a predictive maintenance model on the OctaiPipe platform with the support of P&G. The model will aim to forecast safety incident rates to reduce Health & Safety (H&S) incidents. Raiven will be exploring new Data Science methodologies that allow organisations to work collaboratively and privately, by exploring data abstraction and alignment techniques.

Whilst Site-Assist will identify non-HSE compliant environments and situations in security-sensitive settings, i.e., critical infrastructure to apply the technology and Digital Catapult will assess the AI/ML explainability and verifiability of the solution; driving optimisation and new standards.


OctaiPipe is a revolutionary Federated Edge AI-for-IoT platform that learns the AI model on the edge device, meaning raw data is always kept at source and private. Captured model parameters are then distributed to centralised Federated Learning (FL) server in real-time (<5ms), where contributions are aggregated into a single global model before being fed back to edge devices to benefit from globally aggregated learnings of 1,000s devices. This delivers a unique market offering connecting the customer to Artificial Intelligence of Things (AIoT) that is more private, secure, efficient, and autonomous—enabling scale through automation of trust.

Procter & Gamble (P&G) is one of the world’s largest fast-moving consumer goods companies and home to iconic, trusted brands, including Ariel, Lenor, Flash, Pampers, Always, Pantene, Herbal Essences, Oral B and Gillette. With a large global footprint of 70 countries and with 5 billion consumers worldwide, the design, development, growth and success of these products is driven by innovation and insight of its employees.

Digital Catapult is the UK authority on advanced digital technology with an existing track record of delivering over 50 collaborative R&D projects looking to advance the adoption of advanced digital technologies. Through collaboration and innovation across our specialist programmes and experimental facilities, we accelerate industry adoption of advanced digital technologies to drive growth and opportunity across the economy, making sure that innovation thrives, and the right solutions make it to the real world.

Raiven was formed to bridge the skills and technology gap in industry, and to enable technology adoption across industries. Raiven’s expertise in Artificial Intelligence and Data Science ensure innovations are closely monitored and continue to deliver business value. Raiven aims to be a trusted expert that your business can turn to for advice, strategy and delivery, with a focus on implementing responsible A.I. solutions that are pragmatic, people-centric, ethical and results-oriented.

Site-Assist (SME) is a software service provider of app-based solutions for collecting HSE and productivity data on-site—enabling risk mitigation, supporting compliance, enhancing time efficiency/productivity, and minimising carbon impacts. Site Assist were incorporated in early 2021 when an opportunity for a market entry product was identified around permitting, which was developed and deployed accordingly. Adopted by Balfour Beatty and HS2. Subsequently, other large players have adopted the software to varying degrees; Babcock, AWE, Hinkley Point C, Morgan Sindall, Emcore inter alia.

If you want to learn more about the project, solutions and consortium, join our exclusive webinar on July 16th, where speakers will present:

  1. how predictions can be meaningfully and actionable by working at a sufficient level of granularity,
  2. the technology that now exists to do so, enabling game-changing preventative impact at the workgroup level,
  3. how you can be part of this industry leading consortium.