Incremental Vertical Federated Multi-View Clustering with Asynchronous Client Arrival

Existing vertical federated multi-view clustering methods generally assume that all clients are simultaneously available and that the client set is fixed. In practice, however, each client holds one view of the same samples, while clients may not be simultaneously valid and may join asynchronously over time. This setting exposes two limitations of current approaches. First, they typically require previous clients to participate again, which is infeasible when old clients are no longer available. Even when they remain available, such repeated participation still incurs extra communication and privacy costs. Second, given only the historical global consensus and one newly uploaded label matrix, existing methods lack an effective fusion mechanism. To address these issues, we propose an asynchronous incremental vertical federated multi-view clustering framework. Each arriving client constructs an anchor graph from its own view and learns a local label matrix, which is uploaded to the server. The server then collaborates with the current client to alternately update the global consensus and the local label matrix via an augmented Lagrangian formulation until convergence. Only the current client is involved, and the server retains only the latest consensus. Experiments on multiple datasets confirm the applicability and effectiveness of the proposed framework in incremental scenarios.