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A Multivocal Literature Review on Privacy and Fairness in Federated Learning
Federated Learning presents a way to revolutionize AI applications by eliminating the
necessity for data sharing. Yet, research has shown that information can still be extracted …
necessity for data sharing. Yet, research has shown that information can still be extracted …
Starlit: Privacy-preserving federated learning to enhance financial fraud detection
A Abadi, B Doyle, F Gini, K Guinamard… - arxiv preprint arxiv …, 2024 - arxiv.org
Federated Learning (FL) is a data-minimization approach enabling collaborative model
training across diverse clients with local data, avoiding direct data exchange. However, state …
training across diverse clients with local data, avoiding direct data exchange. However, state …
Is Federated Learning Still Alive in the Foundation Model Era?
N Baracaldo - Proceedings of the AAAI Symposium Series, 2024 - ojs.aaai.org
Federated learning (FL) has arisen as an alternative to collecting large amounts of data in a
central place to train a machine learning (ML) model. FL is privacy-friendly, allowing multiple …
central place to train a machine learning (ML) model. FL is privacy-friendly, allowing multiple …
Advancements in privacy enhancing technologies for machine learning
A Hall - 2024 - napier-repository.worktribe.com
The field of privacy preserving machine learning is still in its infancy and has been growing
in popularity since 2019. Privacy enhancing technologies within the context of machine …
in popularity since 2019. Privacy enhancing technologies within the context of machine …