Bayesian Federated Learning (BFL) enables uncertainty quantification and
robust adaptation in distributed learning. In contrast to the frequentist
approach, it estimates the posterior distribution of a global model, offering
insights into model reliability. However, current BFL methods neglect continual
learning challenges in dynamic environments where data distributions shift over
time. We propose a continual BFL framework applied to human sensing with radar
data collected over several days. Using Stochastic Gradient Langevin Dynamics
(SGLD), our approach sequentially updates the model, leveraging past posteriors
to construct the prior for the new tasks. We assess the accuracy, the expected
calibration error (ECE) and the convergence speed of our approach against
several baselines. Results highlight the effectiveness of continual Bayesian
updates in preserving knowledge and adapting to evolving data.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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