Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2024

Abstract

Federated learning (FL) is a method of training AI systems on different datasets without sharing data. The promise of FL is to enable AI systems to be trained on data, including personal data, while preserving data privacy and confidentiality, and thus, inter alia, facilitate compliance with data protection legislation. FL has generated a considerable interest amongst the computer science community, yet there is a dearth of legal analysis of FL. This is a problem because the question of whether FL facilitates compliance with data protection legislation is a legal question. This article will fill this lacuna by providing a comprehensive legal analysis of FL through an examination of how the EU’s General Data Protection Regulation (GDPR) applies to FL. This article postulates that, from a legal perspective, FL can be an effective method of facilitating compliance with data protection regulations. However, this article expresses doubt that, without support from policy makers and regulators, FL will be used sufficiently widely to make significantly more data available for the training of AI systems, than is currently the case.

Keywords

Federated Learning, artificial intelligence, AI, machine learning, data sharing, AI data problem, privacy protection, GDPR

Discipline

Artificial Intelligence and Robotics | Privacy Law

Research Areas

Innovation, Technology and the Law

Publication

Rutgers Law Journal

Volume

52

First Page

1

Last Page

40

ISSN

0277-318X

Publisher

Rutgers University * School of Law (Camden)

Embargo Period

10-6-2024

Comments

Forthcoming in Volume 52 Rutgers L. Rec. (2024), available at http://www.lawrecord.com. All rights reserved.

Additional URL

http://www.lawrecord.com/

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