解决联邦多视图聚类中的数据不完整与视图冲突问题。
Enhanced Federated Deep Multi-View Clustering under Uncertainty Scenario
- 通过层次对比融合消除客户端内视图语义冲突。
- 在多个数据集上优于现有方法，聚类性能显著提升。
- 适合处理数据不全、视图不一致的现实场景。
传统联邦多视图聚类假设客户端视图一致，但实际中普遍存在视图不完整、冗余或损坏。现有方法虽建模视图异构性，却忽略动态视图组合引发的语义冲突，无法应对双重不确定性：视图不确定性（任意视图配对导致的语义不一致）和聚合不确定性（客户端更新不平衡）。为此，提出增强型联邦深度多视图聚类框架：首先在客户端内进行局部语义对齐，通过层次对比融合解决视图不确定性；引入视图自适应漂移模块，基于全局-局部原型对比动态修正参数偏差以缓解聚合不确定性；设计均衡聚合机制协调客户端更新。实验表明，该方法在多个基准数据集上对异构不确定视图表现出更强鲁棒性，全面超越当前最优基线。
原文摘要 · Abstract (English)
Traditional Federated Multi-View Clustering assumes uniform views across clients, yet practical deployments reveal heterogeneous view completeness with prevalent incomplete, redundant, or corrupted data. While recent approaches model view heterogeneity, they neglect semantic conflicts from dynamic view combinations, failing to address dual uncertainties: view uncertainty (semantic inconsistency from arbitrary view pairings) and aggregation uncertainty (divergent client updates with imbalanced contributions). To address these, we propose a novel Enhanced Federated Deep Multi-View Clustering framework: first align local semantics, hierarchical contrastive fusion within clients resolves view uncertainty by eliminating semantic conflicts; a view adaptive drift module mitigates aggregation uncertainty through global-local prototype contrast that dynamically corrects parameter deviations; and a balanced aggregation mechanism coordinates client updates. Experimental results demonstrate that EFDMVC achieves superior robustness against heterogeneous uncertain views across multiple benchmark datasets, consistently outperforming all state-of-the-art baselines in comprehensive evaluations.
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