在数据异质性下进行自监督联邦学习用于标签稀缺的硅藻分类
Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification
AI总结：
本文研究了在数据异质性下进行自监督联邦学习用于硅藻分类，探讨了阶段特定的数据异质性，并提出PreDi和PreP-WFL方法以提高模型性能。
摘要：
标签稀缺的视觉分类在去中心化和异质数据下是一个基本挑战，尤其是在站点表现出部分重叠类别集时。虽然自监督联邦学习（SSFL）提供了一个有希望的解决方案，但现有研究通常假设预训练和微调过程中数据异质性模式相同。此外，当前的划分方案往往无法生成纯部分类别不相交的数据设置，限制了对现实世界标签空间异质性的可控模拟。在本文中，我们引入SSFL用于硅藻分类作为代表性的现实世界实例，并系统地研究了阶段特定的数据异质性。我们研究了预训练期间不同站点的未标记数据量的交叉变化以及下游微调期间的标签空间错位。为了在可控的环境中研究后者，我们提出了PreDi，一种划分方案，将标签空间异质性分解为两个正交维度，即类别流行度和类别集大小差异，从而可以分别分析其影响。基于由此产生的见解，我们进一步提出了PreP-WFL（基于流行度的个性化加权联邦学习），以适应性地加强低流行度场景中的稀有类别表示。大量实验表明，SSFL在同质和异质设置下均优于本地仅训练。未标记数据量的明显异质性与改进的表示预训练相关，而在标签空间异质性下，流行度主导性能，差异的影响较小。PreP-WFL有效缓解了这种退化，收益随着流行度的降低而增加。这些发现为在去中心化识别系统中表征标签空间异质性提供了机制基础。
英文摘要：
Label-scarce visual classification under decentralized and heterogeneous data is a fundamental challenge in pattern recognition, especially when sites exhibit partially overlapping class sets. While self-supervised federated learning (SSFL) offers a promising solution, existing studies commonly assume the same data heterogeneity pattern throughout pre-training and fine-tuning. Moreover, current partitioning schemes often fail to generate pure partially class-disjoint data settings, limiting controllable simulation of real-world label-space heterogeneity. In this work, we introduce SSFL for diatom classification as a representative real-world instance and systematically investigate stage-specific data heterogeneity. We study cross-site variation in unlabeled data volume during pre-training and label-space misalignment during downstream fine-tuning. To study the latter in a controllable setting, we propose PreDi, a partitioning scheme that disentangles label-space heterogeneity into two orthogonal dimensions, namely class Prevalence and class-set size Disparity, enabling separate analysis of their effects. Guided by the resulting insights, we further propose PreP-WFL (Prevalence-based Personalized Weighted Federated Learning) to adaptively strengthen rare-class representations in low-prevalence scenarios. Extensive experiments show that SSFL consistently outperforms local-only training under both homogeneous and heterogeneous settings. The pronounced heterogeneity in unlabeled data volume is associated with improved representation pre-training, whereas under label-space heterogeneity, prevalence dominates performance and disparity has a smaller effect. PreP-WFL effectively mitigates this degradation, with gains increasing as prevalence decreases. These findings provide a mechanistic basis for characterizing label-space heterogeneity in decentralized recognition systems.
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