提出ECoral方法，让联邦学习持续增量时更高效地保留旧知识。
Exemplar-condensed Federated Class-incremental Learning
- 用生成模型提炼流数据特征，生成信息量一致的代表样本
- 在多个联邦任务上超越现有方法，准确率提升显著
- 适合需要长期学习新类别的分布式系统
我们提出一种名为ECoral的原型浓缩联邦持续增量学习方法，旨在将流数据中真实图像的训练特性提炼为信息丰富的回放样本。该方法克服了传统回放策略在联邦持续学习中因数据信息密度异质性导致的原型选择局限。通过保持汇总样本与原始图像间训练梯度的一致性及对历史任务的关系，有效表征流数据。此外，通过客户端间共享解耦生成模型，降低汇总数据的信息层级异质性。大量实验表明，ECoral优于多个前沿方法，并可无缝集成至多种现有框架以进一步提升性能。
原文摘要 · Abstract (English)
We propose Exemplar-Condensed federated class-incremental learning (ECoral) to distil the training characteristics of real images from streaming data into informative rehearsal exemplars. The proposed method eliminates the limitations of exemplar selection in replay-based approaches for mitigating catastrophic forgetting in federated continual learning (FCL). The limitations particularly related to the heterogeneity of information density of each summarized data. Our approach maintains the consistency of training gradients and the relationship to past tasks for the summarized exemplars to represent the streaming data compared to the original images effectively. Additionally, our approach reduces the information-level heterogeneity of the summarized data by inter-client sharing of the disentanglement generative model. Extensive experiments show that our ECoral outperforms several state-of-the-art methods and can be seamlessly integrated with many existing approaches to enhance performance.
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