Federated learning for healthcare applications
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …
become critical for healthcare tasks like in medical image analysis and human behavior …
Federated learning via meta-variational dropout
Federated Learning (FL) aims to train a global inference model from remotely distributed
clients, gaining popularity due to its benefit of improving data privacy. However, traditional …
clients, gaining popularity due to its benefit of improving data privacy. However, traditional …
Overcoming label shift in targeted federated learning
EL Zec, A Breitholtz, FD Johansson - arXiv preprint arXiv:2411.03799, 2024 - arxiv.org
Federated learning enables multiple actors to collaboratively train models without sharing
private data. This unlocks the potential for scaling machine learning to diverse applications …
private data. This unlocks the potential for scaling machine learning to diverse applications …