Authors: Herbert Woisetschläger, Alexander Erben, Bill Marino, Shiqiang Wang, Nicholas D. Lane, Ruben Mayer, Hans-Arno Jacobsen

Published on: February 05, 2024

Impact Score: 8.35

Arxiv code: Arxiv:2402.05968

Summary

• What is new: First interdisciplinary analysis of the European Union Artificial Intelligence Act’s impact on Federated Learning, incorporating both legal and machine learning perspectives.
• Why this is important: Understanding the implications of the AI Act for Federated Learning, especially considering FL’s focus on data privacy and decentralized learning.
• What the research proposes: Advocating for a research reorientation within the FL community to address new challenges and seize opportunities presented by the AI Act for privacy-centric, decentralized ML systems.
• Results: Identification of significant potential for Federated Learning to become central to AI Act-compliant machine learning systems, highlighting opportunities to counter data bias and strengthen data privacy and security.

Technical Details

Technological frameworks used: Federated Learning (FL)

Models used: Not specified

Data used: Interdisciplinary data including legal analyses and machine learning methodologies

Potential Impact

Businesses in AI and ML sectors, particularly those involved with data privacy, data governance, and decentralized learning models.

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