样本压缩的联邦类增量学习
Exemplar-condensed Federated Class-incremental Learning
AI总结：
本文提出ECoral方法，通过将流式数据蒸馏为信息丰富的排练样本并共享解耦生成模型，解决联邦持续学习中样本信息密度异质性问题，有效缓解灾难性遗忘并提升性能。
摘要：
我们提出样本压缩的联邦类增量学习（ECoral），旨在将流式数据中真实图像的训练特征蒸馏为信息丰富的排练样本。所提方法消除了基于回放的联邦持续学习（FCL）中用于缓解灾难性遗忘的样本选择所存在的局限性。这些局限性尤其与每个汇总数据的信息密度异质性有关。我们的方法保持汇总样本的训练梯度一致性以及与过去任务的关系，从而相比原始图像能够有效表示流式数据。此外，我们的方法通过客户端间共享解耦生成模型，降低了汇总数据在信息层面的异质性。大量实验表明，我们的ECoral优于多种最先进的方法，并且能够与许多现有方法无缝集成以提升性能。
英文摘要：
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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