# Bayesian Federated Learning for Continual Training

arXiv: https://arxiv.org/abs/2504.15328 · HTML: https://mlsanity.com/paper/2504.15328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent](https://mlsanity.com/paper/2009.06419/) (2020)
- [Bayesian Federated Learning: A Survey](https://mlsanity.com/paper/2304.13267/) (2023)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo](https://mlsanity.com/paper/2007.00590/) (2020)
- [On Convergence of Federated Averaging Langevin Dynamics](https://mlsanity.com/paper/2112.05120/) (2021)
- [Partitioned Variational Inference: A Framework for Probabilistic Federated Learning](https://mlsanity.com/paper/2202.12275/) (2022)
- [Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy](https://mlsanity.com/paper/2108.07644/) (2021)
- [A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments](https://mlsanity.com/paper/2103.14546/) (2021)
- [On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks](https://mlsanity.com/paper/2404.18519/) (2024)
- [Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT](https://mlsanity.com/paper/2409.01207/) (2024)
- [Variational Bayes for Federated Continual Learning](https://mlsanity.com/paper/2405.14291/) (2024)

## Abstract (arXiv)

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.