用贝叶斯方法让分布式模型持续学习，适应动态数据变化。
Bayesian Federated Learning for Continual Training
- 基于SGLD的连续贝叶斯更新，利用历史后验作为新任务先验
- 在雷达人体感知任务中，准确率提升12.3%，校准误差降低41%
- 适合需要可靠不确定性估计的长期部署智能系统
贝叶斯联邦学习（BFL）可在分布式学习中实现不确定性量化与鲁棒自适应。与频率学派方法不同，它估计全局模型的后验分布，从而提供模型可靠性信息。然而，现有BFL方法忽略了动态环境中数据分布随时间漂移带来的持续学习挑战。本文提出一种适用于雷达人体感知的持续贝叶斯联邦学习框架，通过随机梯度朗之万动力学（SGLD）实现模型的序列更新，并利用历史后验构建新任务的先验。我们在数日采集的雷达数据上评估了该方法的准确率、期望校准误差（ECE）及收敛速度，结果表明持续贝叶斯更新能有效保留知识并适应演化数据。
原文摘要 · Abstract (English)
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.
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