On-device Learning for resource-constrained 6G Ambient IoT devices

Purpose and goal

One of the visions of 6G is to support a number of IoT devices that far exceed what is possible with current networks like 4G and 5G. However, today’s IoT devices typically rely on models that are trained in the cloud, which means that it is not easy to adapt them to the specifics of the context where they are deployed. The goal of this work is to provide on-device training for such 6G Ambient IoT devices.

Expected effects and result

The expected result is to demonstrate the feasibility of online learning on Ambient IoT devices by designing and implementing an online learning system that enables model training on Ambient IoT devices. On-device training allows (1) training models via local data without sharing data, thus enabling privacy-preserving computation by design, (2) model personalization and environment adaptation, and (3) deploying accurate models in any location without stable internet connectivity.

Planned approach and implementation

After a literature review that studies the theoretical foundations and implementation details of Mondrian Forests, the next step is to implement Mondrian Forests into C, probably using a translation tool from Python. This implementation likely needs to be optimized for the resource-constraints of IoT devices. Finally, the approach will be evaluated, and the thesis will be written and defended.