How to Combine Variational Bayesian Networks in Federated Learning. (arXiv:2206.10897v1 [cs.LG])

Web: http://arxiv.org/abs/2206.10897

Federated Learning enables multiple data centers to train a central model
collaboratively without exposing any confidential data. Even though
deterministic models are capable of performing high prediction accuracy, their
lack of calibration and capability to quantify uncertainty is problematic for
safety-critical applications. Different from deterministic models,
probabilistic models such as Bayesian neural networks are relatively
well-calibrated and able to quantify uncertainty alongside their competitive
prediction accuracy. Both of the approaches appear in the federated learning
framework; however, the aggregation scheme …

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