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Federated Incomplete Multi-View Clustering With Cross-View Relationship Imputation
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AI
DOI：10.1109/TKDE.2026.3692681.png)
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Abstract
En 中文
Deep learning-based multi-view clustering techniques have attracted considerable attention due to their ability to recover missing views in incomplete multi-view scenarios. Nevertheless, in the federated multi-view learning scenario, these techniques are often constrained by the inherently decentralized nature of the data. Consequently, most existing federated incomplete multi-view methods primarily rely on internal correlations within a single view to recover missing data, failing to leverage complex global cross-view dependencies. This inherent limitation renders them particularly vulnerable to high missing rates, leading to a sharp decline in clustering accuracy and substantially restricting their applicability in complex, real-world scenarios. To address this issue, we propose the Federated Incomplete Multi-view clustering framework with Cross-view relationship Imputation, termed FIMCI. Specifically, we employ a Transformer-based encoder at each client and server to capture cross-view relationships, thereby completing missing data recovery and extracting view-specific information. We then design a dynamic view-fusion mechanism at the server, which adaptively assigns view weights and provides feedback to the clients. Furthermore, we implement category-level contrastive learning to enhance the robustness of the consensus representation through pseudo-label generation. In this way, FIMCI explores the consistency and complementarity between views through global view weight allocation and local view encoders, enabling the completion of missing views and clustering tasks while better protecting data privacy. Experimental results on multiple multi-view datasets verify that our method outperforms existing advanced methods in terms of both performance and efficiency.
Keywords:
Incomplete multi-view clustering
federated learning
contrastive learning
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