Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models

no code implementations • 22 Mar 2024 • John Fischer, Marko Orescanin, Justin Loomis, Patrick McClure

Aggregation strategies have been developed to pool or fuse the weights and biases of distributed deterministic models; however, modern deterministic deep learning (DL) models are often poorly calibrated and lack the ability to communicate a measure of epistemic uncertainty in prediction, which is desirable for remote sensing platforms and safety-critical applications.

Federated Learning Uncertainty Quantification +1