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Found 6 papers, 2 papers with code
CAFO: Feature-Centric Explanation on Time Series Classification
This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS.
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation scheme for updating the global model to an adaptive one, in response to the local signals of the participating clients.
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)
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.
Secure and Differentially Private Bayesian Learning on Distributed Data
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries.
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.
