The SustainML Developer Framework v0.2.0, built by eProsima, provides a unified and extensible interface to the backend services and the core SustainML_Library v0.2.0.This release enhances stability, visualization, and hardware integration, while preserving the simple, modular workflow that defines SustainML.
Presented at the Symposium on Foundations of Responsible Computing (FORC 2025), researchers from the University of Copenhagen’s Department of Computer Science (KU) explore how memorization, typically linked to overfitting, can, under certain conditions, be leveraged to improve fairness metrics in AI classification models. Their paper, titled “When Can Memorization Improve Fairness?”, analyzes how key group fairness metrics, statistical parity, equal opportunity, and equalized odds, can be influenced, and in some cases superficially optimized, by memorizing targeted subpopulations.
Researchers from the German Research Center for Artificial Intelligence (DFKI) and ETH Zurich have introduced a novel neural architecture for sensor-based Human Activity Recognition (HAR), spotlighting the potential of Kolmogorov-Arnold Networks (KANs) for low-power, high-accuracy AI. Their paper, “Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition”, offers a new approach to building sustainable AI models optimized for time-series data from inertial sensors.
eProsima is proud to announce the release of SustainML v0.2.0, a major update to its backend and developer framework for sustainable machine learning. This new version strengthens stability, usability, and extensibility across both the backend and the Qt-based front-end, introducing smarter task management, improved data handling, and deeper integration with research partners and frameworks.
The SustainML Framework is designed to provide an energy-optimized hardware solution and a corresponding machine learning (ML) model for a given user-defined problem, while taking into account the carbon footprint associated with training.
To support modularity, scalability, and interoperability, the framework relies on eProsima Fast DDS as its communication middleware. Fast DDS enables distributed components, referred to as Nodes, to exchange data efficiently and consistently across the system.
SustainML is among these nine innovative projects dedicated to creating a sustainable ML framework for Green AI.
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This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070408.
