Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and varying client classes over time. The constraints of storage limitations and privacy concerns confine local models to exclusively access the present data within each learning cycle. Consequently, this restriction induces performance degradation in model training on previous data, termed "catastrophic forgetting". However, existing FCL approaches need to identify or know changes in data distribution, which is difficult in the real world. To release these limitations, this paper directs attention to a broader continuous framework. Within this framework, we introduce Federated Bayesian Neural Network (FedBNN), a versatile and efficacious framework employing a variational Bayesian neural network across all clients. Our method continually integrates knowledge from local and historical data distributions into a single model, adeptly learning from new data distributions while retaining performance on historical distributions. We rigorously evaluate FedBNN's performance against prevalent methods in federated learning and continual learning using various metrics. Experimental analyses across diverse datasets demonstrate that FedBNN achieves state-of-the-art results in mitigating forgetting.

翻译：联邦持续学习（FCL）因其处理现实世界流数据的潜力而受到越来越多的关注，这些数据的特点是数据分布不断演变且客户端类别随时间变化。存储限制和隐私问题的约束使得本地模型在每个学习周期内只能访问当前数据。因此，这种限制导致模型对先前数据的训练性能下降，即所谓的“灾难性遗忘”。然而，现有的FCL方法需要识别或了解数据分布的变化，这在现实世界中是困难的。为了突破这些限制，本文关注一个更广泛的持续学习框架。在此框架内，我们提出了联邦贝叶斯神经网络（FedBNN），这是一个通用且高效的框架，在所有客户端上采用变分贝叶斯神经网络。我们的方法持续地将本地和历史数据分布的知识整合到单一模型中，能够熟练地从新数据分布中学习，同时保持对历史分布的性能。我们使用多种指标，将FedBNN的性能与联邦学习和持续学习中流行的方法进行了严格比较。跨多个数据集的实验分析表明，FedBNN在减轻遗忘方面取得了最先进的结果。