Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-the-art research and present the first comprehensive survey of FCL for Edge-AI. We categorize FCL methods based on three task characteristics: federated class continual learning, federated domain continual learning, and federated task continual learning. For each category, an in-depth investigation and review of the representative methods are provided, covering background, challenges, problem formalisation, solutions, and limitations. Besides, existing real-world applications empowered by FCL are reviewed, indicating the current progress and potential of FCL in diverse application domains. Furthermore, we discuss and highlight several prospective research directions of FCL such as algorithm-hardware co-design for FCL and FCL with foundation models, which could provide insights into the future development and practical deployment of FCL in the era of Edge-AI.

翻译：边缘智能作为边缘计算与人工智能的融合范式，已成为一种前景广阔的技术架构，使得先进的人工智能模型能够部署在靠近用户的网络边缘。在此背景下，联邦持续学习应运而生，其作为一种关键框架，能够在学习新任务的同时融合来自不同客户端的知识，同时保障数据隐私并保留历史任务的知识。通过这种方式，联邦持续学习旨在确保学习模型在动态分布式环境中保持稳定可靠的性能。本综述系统梳理了该领域的前沿研究，首次对面向边缘智能的联邦持续学习进行了全面综述。我们根据任务特性将联邦持续学习方法划分为三类：联邦类持续学习、联邦域持续学习以及联邦任务持续学习。针对每个类别，我们对代表性方法进行了深入探讨与评述，涵盖研究背景、核心挑战、问题形式化、解决方案及现有局限。此外，本文回顾了当前联邦持续学习赋能的实际应用案例，揭示了该方法在不同应用领域的发展现状与潜力。最后，我们探讨并指出了联邦持续学习的若干前瞻性研究方向，例如面向联邦持续学习的算法-硬件协同设计、基于基础模型的联邦持续学习等，这些方向可为边缘智能时代联邦持续学习的未来发展与实际部署提供重要启示。