FedMSGL：一种基于自表达超图的联邦多视图学习
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning
AI总结：
针对联邦学习中特征维度差异及多视图数据特性被忽视的问题，提出FedMSGL，通过本地自表达子空间学习、中心自适应融合与跨视图超图建模提升性能。
摘要：
联邦学习对于在保护数据隐私和安全的同时实现跨去中心化数据源的协同模型训练至关重要。该方法缓解了与集中式数据收集相关的风险，并解决了与数据所有权和合规性有关的担忧。尽管联邦学习算法在解决通信瓶颈和增强隐私保护方面取得了重大进展，但现有工作忽视了数据特征维度差异所产生的影响，导致全局模型不成比例地依赖具有较大特征维度的参与者。此外，当前的单视图联邦学习方法未能考虑多视图数据的独特特征，从而导致处理此类数据时性能欠佳。为解决这些问题，我们提出了一种基于自表达超图的联邦多视图学习方法（FedMSGL）。所提方法在本地训练中利用自表达特性，以学习带有潜在样本关系的统一维度子空间。在中心端，采用自适应融合技术生成全局模型，同时根据学习到的全局子空间和视图特有子空间构建超图，以捕获跨视图的复杂互连。在具有不同特征维度的多视图数据集上进行的实验验证了所提方法的有效性。
英文摘要：
Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized data collection and addresses concerns related to data ownership and compliance. Despite significant advancements in federated learning algorithms that address communication bottlenecks and enhance privacy protection, existing works overlook the impact of differences in data feature dimensions, resulting in global models that disproportionately depend on participants with large feature dimensions. Additionally, current single-view federated learning methods fail to account for the unique characteristics of multi-view data, leading to suboptimal performance in processing such data. To address these issues, we propose a Self-expressive Hypergraph Based Federated Multi-view Learning method (FedMSGL). The proposed method leverages self-expressive character in the local training to learn uniform dimension subspace with latent sample relation. At the central side, an adaptive fusion technique is employed to generate the global model, while constructing a hypergraph from the learned global and view-specific subspace to capture intricate interconnections across views. Experiments on multi-view datasets with different feature dimensions validated the effectiveness of the proposed method.
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