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Found 5 papers, 4 papers with code
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space
To improve scalability for larger models, one common Bayesian approach is to approximate the global predictive posterior by multiplying local predictive posteriors.
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
To make practical, we further devise an offline RL algorithm to approximately find the solution.
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Robust One Round Federated Learning with Predictive Space Bayesian Inference
In contrast, our method performs the aggregation on the predictive posteriors, which are typically easier to approximate owing to the low-dimensionality of the output space.
Personalized Federated Learning via Variational Bayesian Inference
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy.
