TARGET：通过无样本蒸馏实现联邦类别持续学习
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation
AI总结：
针对联邦类别持续学习中新类动态加入且 non-IID 数据加剧遗忘的问题，提出 TARGET，通过旧全局模型蒸馏与生成器合成数据实现无样本、保护隐私的持续学习。
摘要：
本文聚焦一个研究不足但十分重要的问题：联邦类别持续学习（Federated Class-Continual Learning，FCCL），即在联邦学习中动态加入新类别。现有 FCCL 工作存在多种局限，例如需要额外数据集，或需要存储来自先前任务的私有数据。针对这些问题，我们首先证明 non-IID 数据会加剧联邦学习中的灾难性遗忘问题。随后，我们提出一种名为 TARGET（federated class-continual learning via exemplar-free distillation，即通过无样本蒸馏实现的联邦类别持续学习）的新方法，在保护客户端数据隐私的同时缓解 FCCL 中的灾难性遗忘。所提方法在模型层面利用先前训练的全局模型，将旧任务知识迁移到当前任务；此外，在数据层面训练一个生成器以生成合成数据，从而在每个客户端上模拟数据的全局分布。与以往 FCCL 方法相比，TARGET 不需要任何额外数据集，也不需要存储先前任务的真实数据，因此非常适合数据敏感场景。
英文摘要：
This paper focuses on an under-explored yet important problem: Federated Class-Continual Learning (FCCL), where new classes are dynamically added in federated learning. Existing FCCL works suffer from various limitations, such as requiring additional datasets or storing the private data from previous tasks. In response, we first demonstrate that non-IID data exacerbates catastrophic forgetting issue in FL. Then we propose a novel method called TARGET (federat\textbf{T}ed cl\textbf{A}ss-continual lea\textbf{R}nin\textbf{G} via \textbf{E}xemplar-free dis\textbf{T}illation), which alleviates catastrophic forgetting in FCCL while preserving client data privacy. Our proposed method leverages the previously trained global model to transfer knowledge of old tasks to the current task at the model level. Moreover, a generator is trained to produce synthetic data to simulate the global distribution of data on each client at the data level. Compared to previous FCCL methods, TARGET does not require any additional datasets or storing real data from previous tasks, which makes it ideal for data-sensitive scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
