Sustainable Artificial Intelligence (SAI)


Investigating the sustainability of AI and AI for sustainability

Sustainable AI research unit targets a twofold objective: we aim to address the sustainability of developing and using AI systems and, we direct AI usage towards the sustainable development goals (i.e., AI4Good). In other words, for us, it is of upmost importance to design AI systems for sustainable development as well as explicitly targeting the sustainability of AI training and usage.

Our RU is, therefore, multi-disciplinary in nature and spans several research areas including data science, computer science, network science, information engineering, wireless communications, energy engineering, environmental engineering and remote sensing.

Energy-aware AI solutions for sustainable development

Our research activities devise energy-aware, high-accuracy and explainable Machine Learning (ML) models to support data processing from distributed sources such as sensors, wearable devices, mobile phones, base stations and other edge network entities, for system control and optimization.

Our proposals target a holistic approach to learning systems; meaning that our algorithms will be designed to solve multiple learning tasks such as prediction, classification, anomaly detection and automatic control. The discovery of elusive governing dynamics in physical processes is also considered in our analysis.

Decentralized learning is our main area of research. Such approach allows to distribute computing power among several network entities without completely relying on energy-hungry cloud data centres. Our studies focus on the orchestration of such distributed process via Federated learning, Gossip learning as well as Multi-Agent Reinforcement learning. We also consider methods to improve the efficiency  of our ML models via Knowledge Transfer learning, Meta-learning and Continual learning.

Sustainable Artificial Intelligence | Research units | CTTC

Given the distributed nature of our methodology, communications and networking among learning nodes play a key role. For this, ML-based semantic communication is studied to maximize the communication system capacity by capitalizing on the largely untapped intrinsic attributes of the conveyed information. Moreover, Blockchain technology is analysed and tailored into sustainable learning systems to achieve high reliability and security during the learning process iterations.

An additional goal of our methodology is to provide explainable insight and interpretability into a system’s behaviour  by introducing appropriate observational, inductive, or learning biases that steer the learning process towards identifying physically consistent solutions.

Research lines

The objective of this line is to study, design and implement methods enabling distributed and federated training and inference of ML models at the edge nodes. Hierarchical as well as totally decentralized architectures are taken into consideration.

Our approach is to natively include energy efficiency in the design of our methods via, e.g., knowledge transfer learning, continual learning, meta-learning, interpretability, semantic compression and communication.

We will use tools such as high-dimensional probability and optimization to study the tradeoff between model/data compression and performance. Moreover, given the distributed nature of our methodology, blockchain technology will be tailored into sustainable learning systems.

The main objective of this line is to deploy ML models into computing platforms to cover specific use cases which may span multiple areas and be classified as follows:

  • Sustainable computing for networking: low-complex and energy-aware learning methods enabling characterization of the mobile traffic demands, network resource optimization, intelligent decision making.
  • Sustainable computing for societal challenges: distributed and energy-aware learning methods to support sustainable development goals (e.g., climate change, sustainable cities and communities, affordable and clean energy, life on land, life below water, industry, innovation and infrastructure).