Edge Learning

Edge learning is an approach in AI that shifts data processing closer to the source—enabling faster, more efficient, and privacy-conscious solutions. Today, edge learning is a core component of AI and a foundational element in several of AI Sweden's ongoing projects.

Examples of related projects

AI Sweden brings together technology developers, industry partners tackling real-world challenges, and researchers driving innovation across domains such as finance, mobility, health, manufacturing, and energy. By leveraging edge learning, we can enable privacy-preserving, collaborative AI solutions that operate close to the data source—pushing performance and efficiency to new levels.

What is edge learning?

Decentralized AI and edge learning refer to moving intelligence and learning out to different devices and organizations. We can train machine learning models on locally available (i.e. decentralized) data and make local decisions. The approach enables combining knowledge from several local datasets, without distributing the actual raw data between devices, locations, and organizations. Unlike algorithms trained on a centralized dataset, decentralized learning distributes models rather than the data itself.

In federated learning, local models trained in edge devices are typically aggregated at a central location.

Create initial model
Transmit current model to devices
Training on devices using local data
Transmit local model parameters to aggregator
Aggregate parameters to create updated model
Send latest model for further training or deployment

A picture of a model of federated learning

In swarm learning, one of the edge devices is also used as an aggregator.

Devices register on network
Devices receive initial model
Devices train model on local data
Devices share and merge models
Repeat steps 3-4 until satisfied

A picture displaying a model of swarm learning

What are the benefits of edge learning?

Traditional centralized AI applications have evolved over the past few decades to
reshape how we live and move. We are familiar with voice assistants (e.g., Siri, Alexa, and Google Assistant). Active driver assistance and automated driving features are increasing in prevalence to heighten safety and reduce the load on drivers. The same trend is visible in healthcare, where assisted diagnostics are used to assist physicians in filtering, identifying and correctly diagnosing medical conditions.

Topics such as AI ethics, data privacy, data security, data ownership, data transfer, computing, and storage costs are concerns for businesses, the public, and governments. Countries have begun to create policies that enhance local vs. global learning (such as European or Chinese data protection regulations) or otherwise limit the long-term shared open use of data. At the same time, stakeholders are increasingly worried about AI bias and argue strongly for the need for more diversified and less restrictive modeling.

By moving learning to the edge, organizations can begin to collaborate globally while addressing concerns around data security, data privacy, and data transfer barriers. At the same time, they benefit from leveraging otherwise unused computational resources to solve growing problems with the shortcomings of data-starved modeling.

Explore cutting-edge AI solutions

AI Sweden provides a Testbed where partner organizations can collaborate, test, and explore cutting-edge AI solutions. It is continuously evolving and offers a hybrid environment — combining physical infrastructure in Gothenburg (pictured below), Linköping, and Helsingborg with digital and cloud-based components.

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