Assessing the Sustainability and Trustworthiness of Federated Learning Models

Abstract

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.

Keywords: Sustainable AI, Carbon Footprint, Federated Learning, Distributed Machine Learning