标题： 基于扩散模型的数据合成辅助联邦半监督学习 显示英文标题

标题： Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning

摘要： 联邦半监督学习（FSSL）主要受到两个因素的挑战：客户端之间标记数据的稀缺性以及客户端之间数据的非独立同分布（non-IID）特性。 在本文中，我们提出了一种新方法，基于扩散模型的数据合成辅助联邦半监督学习（DDSA-FSSL），该方法利用扩散模型（DM）生成合成数据，弥合异构本地数据分布与全局数据分布之间的差距。 在DDSA-FSSL中，客户端通过使用联邦学习训练的分类器对未标记数据进行伪标记，以解决标记数据稀缺的问题。 然后，使用标记数据和精度优化的伪标记数据协同训练DM，使客户端能够为在其标记数据集中缺失的类别生成合成样本。 此过程使客户端能够生成更全面的与全局分布一致的合成数据集。 在多个数据集和不同non-IID分布上进行的大量实验表明了DDSA-FSSL的有效性，例如，在具有10%标记数据的CIFAR-10数据集上，准确率从38.46%提高到了52.14%。 显示英文摘要

摘要： Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we propose a novel approach, diffusion model-based data synthesis aided FSSL (DDSA-FSSL), which utilizes a diffusion model (DM) to generate synthetic data, bridging the gap between heterogeneous local data distributions and the global data distribution. In DDSA-FSSL, clients address the challenge of the scarcity of labeled data by employing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. This process allows clients to generate more comprehensive synthetic datasets aligned with the global distribution. Extensive experiments conducted on multiple datasets and varying non-IID distributions demonstrate the effectiveness of DDSA-FSSL, e.g., it improves accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.