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Learning from local, real-time data

The privacy-safe path to smarter, greener buildings
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Energy-smart buildings with federated learning technology

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.
Data Challenges
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.
Why Federated Learning
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. 

Creating strong data federations

Data collaborations for unlocking collective value.
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Air traffic management is moving towards a digital European sky

Business Need
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.
Data Challenges
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.
Why Federated Learning
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.

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Edge learning

Manage large scale edge learning

Learn from individual, group, or fleet data.
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Reduce latency and cost in EdgeAI

Business Need
Autonomous driving systems need to enable federated on-device training of models in order to manage large scale, in-vehicle machine learning
Data Challenges
Collecting data from all cars in use is expensive and in many cases impossible due to connection problems and the sheer quantity of data generated by modern cars.
Why Federated Learning
Federated Learning addresses the challenges by training on-board machine learning models in a federated setting so that each single car can learn from individual, group and fleet data.

Learn more: AI Sweden EdgeLab
Medical AI

Enable ML in complex environments

Improving machine learning models with distributed data.
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Medical AI

Business Need
There is a large potential in using AI in medicine. Use case include digital pathology, segmentation of organs and tumor, and detecting anomalies in time series data. Today doctors manually generate annotated training data. The software needs to learn from this user generated data to improve machine learning models. Without the data, they do not reach the required scale of data to train high performing models.
Data Challenges
The problem is that the data is sensitive, owned by the customers, and subject to health data regulations. Data cannot be be moved off site and shared, and cannot be used effectively to improve AI software. Single clinics struggle with obtaining diverse enough data in large enough quantities. Anonymization and pooling of data is both costly, time consuming, and risks reducing data utility.
Why Federated Learning
With federated learning the regulatory challenge can be addressed and no data is moved off site. Without sharing the data, learnings from multiple clinics can be integrated to improve the machine learning models.

Learn more: Itea Assist
product development

Leverage our technology in your organisation

Custom product development based on Scaleout Platform.
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SciLifeLab Serve

SciLifeLab, the leading Swedish infrastructure for data-driven life science, is building a collaborative, open source solution to enable the life science community in Sweden accelerate use of AI.

Essential needs are model serving and sharing, and delivery of various AI tools from on-premise infrastructure.

Together with the team at the SciLifeLab data center we are building a solution based on Scaleout platform, deployed to Kubernetes.

Tooling include Jupyter, MLFlow, RStudio, RShiny, Tensorflow Serving, TorchServe, Dash and Flask.

Learn more about SciLifeLab Serve

Let's talk about federated learning in your company.

Learn more about how to leverage siloed, business sensitive and regulated data in your machine learning pipelines. Request a consultation today.
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