Federated Learning Priorities Under the European Union Artificial
  Intelligence Act
Herbert Woisetschläger Alexander Erben Bill Marino Shiqiang Wang Nicholas D. Lane Ruben Mayer Hans-Arno Jacobsen
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