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Artificial intelligence (AI) plays a pivotal role in various sectors,influencing critical decision-making processes in our daily lives. Within theAI landscape, novel AI paradigms, such as Federated Learning (FL), focus onpreserving data privacy while collaboratively training AI models. In such acontext, a group of experts from the European Commission (AI-HLEG) hasidentified sustainable AI as one of the key elements that must be considered toprovide trustworthy AI. While existing literature offers several taxonomies andsolutions for assessing the trustworthiness of FL models, a significant gapexists in considering sustainability and the carbon footprint associated withFL. Thus, this work introduces the sustainability pillar to the most recent andcomprehensive trustworthy FL taxonomy, making this work the first to addressall AI-HLEG requirements. The sustainability pillar assesses the FL systemenvironmental impact, incorporating notions and metrics for hardwareefficiency, federation complexity, and energy grid carbon intensity. Then, thiswork designs and implements an algorithm for evaluating the trustworthiness ofFL models by incorporating the sustainability pillar. Extensive evaluationswith the FederatedScope framework and various scenarios varying federationparticipants, complexities, hardware, and energy grids demonstrate theusefulness of the proposed solution.

Assessing the Sustainability and Trustworthiness of Federated Learning Models