A Path Towards Fair and Equitable AI Advancing Bias Mitigation in Federated Learning
Résumé
Machine learning has become ubiquitous across various fields as it aids in data analysis and decision-making. The rise of big data has led to the development of decentralized machine learning solutions for increased efficiency. Regulations like GDPR have emerged to safeguard data privacy. In response to security and privacy concerns, Google introduced Federated Learning (FL) in 2016, which holds promise for preserving privacy in ML. However, FL also poses challenges such as addressing bias and ensuring fairness in AI models.
Domaines
Intelligence artificielle [cs.AI]
Fichier principal
WISC2023_IEEE_Poster_Ferraguig_Lynda_.pdf (387.67 Ko)
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WISC_23_PhD_Forum_Ferraguig_.pdf (712.29 Ko)
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Origine | Fichiers produits par l'(les) auteur(s) |
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Origine | Fichiers produits par l'(les) auteur(s) |
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