27 citations · 41 across the 2 of their papers we have counts for
5 papers
Decentralized federated learning of deep neural networks on non-iid data
Noa Onoszko, Gustav Karlsson, Olof Mogren +1
We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting. More specifically, we study decentralized federated learning, a peer-to-…
Scaling Federated Learning for Fine-tuning of Large Language Models
Agrin Hilmkil, Sebastian Callh, Matteo Barbieri +3
Federated learning (FL) is a promising approach to distributed compute, as well as distributed data, and provides a level of privacy and compliance to legal frameworks. This makes…
Specialized federated learning using a mixture of experts
Edvin Listo Zec, Olof Mogren, John Martinsson +2
In federated learning, clients share a global model that has been trained on decentralized local client data. Although federated learning shows significant promise as a key approac…
Adversarial representation learning for private speech generation
David Ericsson, Adam Östberg, Edvin Listo Zec +2
As more and more data is collected in various settings across organizations, companies, and countries, there has been an increase in the demand of user privacy. Developing privacy…
Adversarial representation learning for synthetic replacement of private attributes
John Martinsson, Edvin Listo Zec, Daniel Gillblad +1
Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the rig…