解决医疗数据异构下的联邦学习遗忘问题，提升模型泛化能力。
Federated Impression for Learning with Distributed Heterogeneous Data
- 通过生成全局信息的合成数据缓解本地训练中的灾难性遗忘。
- 在BloodMNIST和Retina数据集上最高提升20%分类准确率。
- 适合处理多中心医疗数据、标签不平衡与领域偏移场景。
基于深度学习的分类方法在真实临床应用中可能不可行，因其需集中所有样本。联邦学习（FL）允许在不共享数据的前提下从分布式数据中学习，缓解隐私与数据所有权问题。由于不同医疗机构的数据采集协议和患者群体差异，数据异构常导致收敛不佳。本研究实验表明，数据异构会引发本地训练中的灾难性遗忘。为此提出FedImpres，通过每轮通信后提取的全局模型，生成代表全局信息的合成数据作为“联邦印象”，与本地数据结合以增强泛化能力。大量实验显示，该方法在含标签不平衡和领域偏移的BloodMNIST与Retina数据集上达到当前最优性能，分类准确率最高提升20%。
原文摘要 · Abstract (English)
Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data, which can help mitigate privacy and data ownership issues. In FL, sub-optimal convergence caused by data heterogeneity is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. Through experimentation in this study, we show that data heterogeneity leads to the phenomenon of catastrophic forgetting during local training. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression. To achieve this, we distill the global model resulting from each communication round. Subsequently, we use the synthetic data alongside the local data to enhance the generalization of local training. Extensive experiments show that the proposed method achieves state-of-the-art performance on both the BloodMNIST and Retina datasets, which contain label imbalance and domain shift, with an improvement in classification accuracy of up to 20%.
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