Strategic Advantage: Our Expertise, Your Success

Buildings; our homes, shopping malls, factories, office buildings, hospitals, transportation hubs and schools account for as much as 37% of carbon emissions globally. Add to that the worrisome development that energy consumption is forecasted to grow by 70% until 2030 driven by population growth and increased living standard of the poor.

Settling  news in this context  is that the energy consumption of buildings, and the carbon emissions they cause, can be dramatically reduced, up to 30 %, through sensors connected to smart systems to optimize lighting, heating, cooling, and other energy-consuming activities. These smart systems are based on artificial intelligence trained on sensor data from buildings.

Smart buildings generate vast amounts of data from sensors and connected devices. Conventional techniques that centralize this data for analysis are not applicable in all cases due to various data transfer barriers:
• regulations such as regional data transfer legislations,
• the data might be sensitive from a business perspective and/or owned by someone else,
• or, the volume of data might simply be so large that data transfer becomes a practical problem.

Federated learning addresses these challenges by enabling local processing of data and model training on edge hardware (e.g. gateway servers in the buildings). By learning from localized data, federated learning not only upholds user privacy but can also craft baseline models that effectively learn from different  buildings or building zones. These  generalizable models can then be further optimized for specific buildings and homes. 

Furthermore, this machine learning framework enables devices to collaboratively learn from each other, increasing the overall network's capabilities to optimize energy efficiency across multiple buildings. Last but not least, the concept of federated learning enables better management of combined geographical or property-specific models which can be adapted for new homes and buildings that lack prior training data. 

The Air Traffic Management (ATM) industry is moving towards a digital European sky. Trajectory Based Operations (TBO) allows for the proper coordination of ATM constraints on traffic, before or during flight and the airspace users can fly the best trajectory possible safely and efficiently. The goal is to have more accurate trajectory predictions.

TBO consist of seamless accurate prediction & optimisation of trajectories and ATM constrains through all the planning phases. Important relevant data spread out over several stakeholders in TBO is non-sharable data: too sensitive business data or protected by GDPR.

Federated Learning addresses these challenges by enabling privacy- preserving exploitation of private data (relevant for operations) for ML purposes, while stakeholders keeping the full ownership and control in their own data silo.

Learn more: https://www.aichain-h2020.eu/