Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909252106321920 |
|---|---|
| author | Woisetschläger, Herbert Mertel, Simon Krönke, Christoph Mayer, Ruben Jacobsen, Hans-Arno |
| author_facet | Woisetschläger, Herbert Mertel, Simon Krönke, Christoph Mayer, Ruben Jacobsen, Hans-Arno |
| contents | The European Union Artificial Intelligence Act mandates clear stakeholder responsibilities in developing and deploying machine learning applications to avoid substantial fines, prioritizing private and secure data processing with data remaining at its origin. Federated Learning (FL) enables the training of generative AI Models across data siloes, sharing only model parameters while improving data security. Since FL is a cooperative learning paradigm, clients and servers naturally share legal responsibility in the FL pipeline. Our work contributes to clarifying the roles of both parties, explains strategies for shifting responsibilities to the server operator, and points out open technical challenges that we must solve to improve FL's practical applicability under the EU AI Act. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08105 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis Woisetschläger, Herbert Mertel, Simon Krönke, Christoph Mayer, Ruben Jacobsen, Hans-Arno Artificial Intelligence K.5; I.2.11; C.2.4; D.2.1 The European Union Artificial Intelligence Act mandates clear stakeholder responsibilities in developing and deploying machine learning applications to avoid substantial fines, prioritizing private and secure data processing with data remaining at its origin. Federated Learning (FL) enables the training of generative AI Models across data siloes, sharing only model parameters while improving data security. Since FL is a cooperative learning paradigm, clients and servers naturally share legal responsibility in the FL pipeline. Our work contributes to clarifying the roles of both parties, explains strategies for shifting responsibilities to the server operator, and points out open technical challenges that we must solve to improve FL's practical applicability under the EU AI Act. |
| title | Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis |
| topic | Artificial Intelligence K.5; I.2.11; C.2.4; D.2.1 |
| url | https://arxiv.org/abs/2407.08105 |