@inproceedings{8cb814f97eab4bf3b53ff4c3b8fe2589,
title = "FLAIRS: Federated Learning AI Regulatory Sandbox",
abstract = "The European Commission{\textquoteright}s new regulatory framework, the Artificial Intelligence (AI) Act, has significant implications for the development of AI. The AI Act defines a set of strict requirements for high-risk AI systems, increasing the regulatory and compliance requirements on developers, providers, and importers of such systems. In this work, we present a comprehensive analysis of the effects of the key provisions of the AI Act on AI systems, and how federated learning, a machine learning paradigm gaining prominence due to its collaborative privacy-preserving approach, can mitigate these effects. We propose a Federated Regulatory Sandbox that eases the burden on developers by providing a way to train foundational models that facilitates compliance with regulations.",
keywords = "AI Act, Federated Learning, Regulatory Sandbox",
author = "Mary Roszel and Beltran Fiz and Radu State",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",
year = "2025",
doi = "10.1007/978-3-031-74630-7_31",
language = "English",
isbn = "9783031746291",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "439--449",
editor = "Rosa Meo and Fabrizio Silvestri",
booktitle = "Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers",
address = "Germany",
}