The increasing integration of Artificial Intelligence (AI) into diverse applications calls for innovative approaches that emphasize efficiency, sustainability, and scalability to reduce the associated environmental costs (Green AI) while extending the range of devices capable of running this kind of technology. This issue is particularly evident in the domains of Internet of Things (IoT) and Computer Vision systems, where computational and energy constraints demand tailored solutions.The increasing complexity of AI systems and models, with their growing numbers of parameters and hyperparameters, has driven exponential increases in resource requirements for training and deployment.


On the other hand, many applications have become too computationallydemanding to be executed on devices with limited processing power, creating a significant barrier to widespread adoption.The concept of Green AI provides a framework for addressing these challenges. It includes strategies for designing energy-efficient Machine Learning (ML) and Deep Learning (DL) models (green-in AI) and leveraging AI to promote sustainable practices in other fields (green-by AI).These efforts are supported by tools that measure and optimise energy consumption, alongside explainable AI (XAI) techniques to better understand and enhance system efficiency.

Lightweight models can enable these technologies to operate effectively in resource constrained environments while minimizing computational costs. The size and inference speed of AI models are often overlooked when proposing solutions that rely on ensembles of models with millions of parameters. Such approaches are impractical for systems that need to process vast amounts of images and videos daily, as seen in applicationslike social network content analysis (e.g., detecting fake images and videos) or real-time scenarios requiring immediate responses.This workshop aims to bring together researchers and practitioners to exchange insights, strategies, and tools for advancing environmentally sustainable and efficient AI technologies.

The focus spans the design and deployment of lightweight models capable of addressing computational and energy constraints in resource-constrained domains, such as IoT and Computer Vision systems.

- Energy-Efficient AI Models for IoT and Edge Devices.

- Lightweight Machine/Deep Learning Models for Real-Time and Computer Vision Applications.

- Lightweight Machine/Deep Learning Models for Resource-Constrained Devices.

- Low-Resource AI for Computer Vision-Based Applications.

- Low-Resource AI for IoT and Embedded Systems.

- Optimization of Machine/Deep Learning Models for Energy and Computational Efficiency.

- Scalable AI Architectures for Low-Power Devices and Embedded Systems

- AI for Sustainable Practices in IoT Systems.

- Green-by-AI: AI Applications to Promote Sustainability in Other Domains (i.e., smart agriculture, healthcare, etc.).

- AI for Real-Time Applications and Optimisation of Resource Usage.

- Explainable AI (XAI) Techniques for Enhancing Efficiency and Transparency.

- Energy-Efficient Machine/Deep Learning in Mobile and Wearable Devices.

- AI Solutions for Deepfake Detection with Low Computational Overhead.

- Lightweight AI Models for Real-Time Deepfake Detection.

- AI-Based Security Systems with Low Computational Overhead.

- Lightweight Biometrics Recognition Systems for Secure Authentication.

- AI-Driven Solutions for Enhancing Energy Efficiency in Distributed Networks.

- Privacy-Preserving AI Models for Sustainable Data Processing.

Main Conference website: https://ecai2025.org/workshops/
Workshop web-site: https://sailsworkshop2025.wixsite.com/sails