联邦持续学习：分布式和非平稳数据上的终身与隐私保护学习综述
Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
AI总结：
本文系统综述联邦持续学习（FCL），定义问题、分析经典联邦学习在非平稳数据下的局限，提出多维分类法，并讨论应用、评估指标及开放挑战。
摘要：
联邦学习（FL）能够在分布式客户端之间实现协作和隐私保护的模型训练，但大多数现有的FL系统隐含地假设数据是平稳的。在现实场景中——如医疗、工业物联网（IIOT）、网络安全和智慧城市——数据流本质上是非平稳的，导致经典FL方法遭受性能下降、不稳定和灾难性遗忘。持续学习（CL）解决了在演化数据分布下的学习问题，但主要在集中式环境中研究，忽视了联邦系统的关键约束，包括隐私、有限通信和客户端异质性。联邦持续学习（FCL）出现在FL和CL的交汇处，旨在支持分布式和非平稳数据上的终身、自适应和隐私感知学习。本综述提供了FCL的全面和系统概述。我们首先给出FCL问题的正式定义并阐明其独特特征。然后分析经典FL在非平稳条件下的局限性，强调CL原理如何支持长期适应。为了组织快速增长的文献，我们提出了FCL方法的多维分类法。此外，我们回顾了代表性的应用领域和数据模态，总结了常用的评估指标，并讨论了评估长期性能和遗忘的实验视角。最后，我们强调了关键开放挑战，包括处理时间漂移下的极端异质性、设计可扩展且隐私保护的记忆机制，以及建立标准化基准。本综述旨在为推进FCL走向鲁棒和可部署的现实世界系统提供参考和路线图。
英文摘要：
Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity. In real-world settings-such as healthcare, industrial IoT (IIOT), cybersecurity, and smart cities-data streams are inherently non-stationary, leading classical FL methods to suffer from performance degradation, instability, and catastrophic forgetting. Continual Learning (CL) addresses learning under evolving data distributions but has been largely studied in centralized settings, overlooking key constraints of federated systems, including privacy, limited communication, and client heterogeneity. Federated Continual Learning (FCL) emerges at the intersection of FL and CL, aiming to support lifelong, adaptive, and privacy-aware learning over distributed and non-stationary data. This survey provides a comprehensive and systematic overview of FCL. We first present a formal definition of the FCL problem and clarify its distinctive characteristics. We then analyze the limitations of classical FL under non-stationary conditions, highlighting how CL principles support long-term adaptation. To organize the rapidly growing literature, we propose a multi-dimensional taxonomy of FCL approaches. Furthermore, we review representative application domains and data modalities, summarize commonly used evaluation metrics, and discuss experimental perspectives for assessing long-term performance and forgetting. Finally, we highlight key open challenges, including handling extreme heterogeneity under temporal drift, designing scalable and privacy-preserving memory mechanisms, and establishing standardized benchmarks. This survey aims to serve as a reference and a roadmap for advancing FCL toward robust and deployable real-world systems.
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