Federated Learning Priorities Under the European Union Artificial Intelligence Act
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916123181580288 |
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| author | Woisetschläger, Herbert Erben, Alexander Marino, Bill Wang, Shiqiang Lane, Nicholas D. Mayer, Ruben Jacobsen, Hans-Arno |
| author_facet | Woisetschläger, Herbert Erben, Alexander Marino, Bill Wang, Shiqiang Lane, Nicholas D. Mayer, Ruben Jacobsen, Hans-Arno |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05968 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning Priorities Under the European Union Artificial Intelligence Act Woisetschläger, Herbert Erben, Alexander Marino, Bill Wang, Shiqiang Lane, Nicholas D. Mayer, Ruben Jacobsen, Hans-Arno Machine Learning Artificial Intelligence Computers and Society Distributed, Parallel, and Cluster Computing I.2; I.2.11; K.5 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 |
| title | Federated Learning Priorities Under the European Union Artificial Intelligence Act |
| topic | Machine Learning Artificial Intelligence Computers and Society Distributed, Parallel, and Cluster Computing I.2; I.2.11; K.5 |
| url | https://arxiv.org/abs/2402.05968 |