Artificial intelligence (AI) plays a pivotal role in various sectors, influencing critical decision-making processes in our daily lives. Within the AI landscape, novel AI paradigms, such as Federated Learning (FL), focus on preserving data privacy while collaboratively training AI models. In such a context, a group of experts from the European Commission (AI-HLEG) has identified sustainable AI as one of the key elements that must be considered to provide trustworthy AI. While existing literature offers several taxonomies and solutions for assessing the trustworthiness of FL models, a significant gap exists in considering sustainability and the carbon footprint associated with FL. Thus, this work introduces the sustainability pillar to the most recent and comprehensive trustworthy FL taxonomy, making this work the first to address all AI-HLEG requirements. The sustainability pillar assesses the FL system environmental impact, incorporating notions and metrics for hardware efficiency, federation complexity, and energy grid carbon intensity. Then, this work designs and implements an algorithm for evaluating the trustworthiness of FL models by incorporating the sustainability pillar. Extensive evaluations with the FederatedScope framework and various scenarios varying federation participants, complexities, hardware, and energy grids demonstrate the usefulness of the proposed solution.

翻译：人工智能（AI）在众多领域发挥着关键作用，影响着我们日常生活中的关键决策过程。在AI领域中，诸如联邦学习（FL）等新型AI范式致力于在协同训练AI模型的同时保护数据隐私。在此背景下，欧盟委员会专家小组（AI-HLEG）已将可持续AI确定为提供可信赖AI必须考虑的关键要素之一。尽管现有文献为评估FL模型的可信性提供了多种分类体系和解决方案，但在考虑可持续性及其相关的碳足迹方面仍存在显著空白。因此，本工作将可持续性支柱引入最新且最全面的可信赖FL分类体系，成为首个涵盖AI-HLEG全部要求的研究。该可持续性支柱评估FL系统的环境影响，纳入了硬件效率、联邦复杂度及能源电网碳强度等概念与指标。进而，本工作设计并实现了一种通过整合可持续性支柱来评估FL模型可信性的算法。利用FederatedScope框架，针对不同联邦参与者、复杂度、硬件及能源电网场景开展的大量评估，证明了所提解决方案的有效性。