Federated Learning Priorities Under the European Union Artificial Intelligence Act
This addresses regulatory challenges for AI practitioners and researchers, but it is an incremental position paper focusing on interdisciplinary analysis rather than new technical methods.
The paper analyzes how the European Union Artificial Intelligence Act (AI Act) could impact Federated Learning (FL), arguing that the regulation may drive FL adoption by prioritizing data privacy and compliance, with opportunities to address data bias and enhance secure computation.
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose starting point of prioritizing data privacy while performing ML fundamentally differs from that of centralized learning. We believe the AI Act and future regulations could be the missing catalyst that pushes FL toward mainstream adoption. However, this can only occur if the FL community reprioritizes its research focus. In our position paper, we perform a first-of-its-kind interdisciplinary analysis (legal and ML) of the impact the AI Act may have on FL and make a series of observations supporting our primary position through quantitative and qualitative analysis. We explore data governance issues and the concern for privacy. We establish new challenges regarding performance and energy efficiency within lifecycle monitoring. Taken together, our analysis suggests there is a sizable opportunity for FL to become a crucial component of AI Act-compliant ML systems and for the new regulation to drive the adoption of FL techniques in general. Most noteworthy are the opportunities to defend against data bias and enhance private and secure computation