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Found 4 papers, 1 papers with code
Connecting Low-Loss Subspace for Personalized Federated Learning
Due to the curse of statistical heterogeneity across clients, adopting a personalized federated learning method has become an essential choice for the successful deployment of federated learning-based services.
Ranked #1 on
Personalized Federated Learning
on MNIST
(ACC@1-100Clients metric)
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data
PhysioNet2012, a dataset for prediction of mortality of patients in an Intensive Care Unit (ICU), was used to verify the performance of the proposed method.
Secure and Differentially Private Bayesian Learning on Distributed Data
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries.
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model
Unlike conventional federated learning algorithms based on gradients, our framework does not require to disassemble a model (i. e., to linear components) or to perturb data (or encryption of data for aggregation) to preserve privacy.
