Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings

As the Internet of Things (IoT) increasingly empowers the network extremes with in-place intelligence through Machine Learning (ML), energy consumption and carbon emissions become crucial factors. ML is often computationally intensive, with state-of-the-art model architectures consuming significant energy per training round and imposing a large carbon footprint. This work, therefore, argues for the need to introduce novel mechanisms into the ML pipelines of IoT services, so that energy awareness is integrated in the decision-making process for when and where to initiate ML model training.