Catastrophic forgetting (CF) poses a persistent challenge in continual learning (CL), especially within federated learning (FL) environments characterized by non-i.i.d. time series data. While existing research has largely focused on classification tasks in vision domains, the regression-based forecasting setting prevalent in IoT and edge applications remains underexplored. In this paper, we present the first benchmarking framework tailored to investigate CF in federated continual time series forecasting. Using the Beijing Multi-site Air Quality dataset across 12 decentralized clients, we systematically evaluate several CF mitigation strategies, including Replay, Elastic Weight Consolidation, Learning without Forgetting, and Synaptic Intelligence. Key contributions include: (i) introducing a new benchmark for CF in time series FL, (ii) conducting a comprehensive comparative analysis of state-of-the-art methods, and (iii) releasing a reproducible open-source framework. This work provides essential tools and insights for advancing continual learning in federated time-series forecasting systems.

翻译：灾难性遗忘（CF）在持续学习（CL）中构成了一个持续存在的挑战，尤其是在以非独立同分布时间序列数据为特征的联邦学习（FL）环境中。尽管现有研究主要集中在视觉领域的分类任务上，但在物联网和边缘应用中普遍存在的基于回归的预测设置仍未得到充分探索。本文提出了首个专门用于研究联邦持续时间序列预测中CF的基准测试框架。利用北京多站点空气质量数据集，在12个去中心化客户端上，我们系统评估了多种CF缓解策略，包括重放、弹性权重巩固、无遗忘学习以及突触智能。主要贡献包括：（i）为时间序列FL中的CF引入了一个新的基准；（ii）对最先进的方法进行了全面的比较分析；（iii）发布了一个可复现的开源框架。这项工作为推进联邦时间序列预测系统中的持续学习提供了必要的工具和见解。