Federated Learning Priorities Under the European Union Artificial Intelligence Act

2024/02/05 by Herbert Woisetschläger, Alexander Erben, Woisetschläger, Herbert +11 · 3 citations
Economics, Econometrics and Finance · Social Sciences · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Digital Transformation in Law #Distributed #European Criminal Justice and Data Protection #FOS: Computer and information sciences #I.2 #I.2.11 #K.5 #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2402.05968

openalex publication_date 2024/02/05 · openalex created_date 2024/02/13 · openalex updated_date 2026/07/28

Abstract

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

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• Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
• FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health
• Federated Learning from Molecules to Processes: A Perspective

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