Edge learning has the potential to revolutionize the development of AI for space applications and AI Sweden’s Space Lab is exploring technologies that can be used for high-impact use cases.
Today, AI models are trained by transferring as much data as possible from sensors to a central storage and compute location. In general, the more data, the better the quality of the resulting model. However, as the number of satellites increases, the available bandwidth to transfer data will become less and less. Thus, data transfer costs are expected to rise, it will take longer to transfer enough data to Earth to train models, and the models will be less accurate if less data is transferred.
Edge learning (also known as decentralized learning, federated learning, or swarm learning) is a technology that has the potential to revolutionize the development of AI for space applications by solving these challenges. This would save both money and resources since there is no need to send large amounts of data to the Earth.