Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization

Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted to a central server, where they are securely aggregated and refined through secure multi-party computation (SMPC) until global convergence is achieved. Within each client, DHBCPSO-MSR employs a dual-layer FS strategy. The inner layer constructs sample and label similarity graphs, generates Laplacian matrices to capture the manifold structure between samples and labels, and applies $L_{2,1}$-norm regularization to sparsify the feature subset, yielding an optimized feature weight matrix. The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset. The updated weight matrix is then fed back to the inner layer for further optimization. Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.