  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.
