Multi-label feature selection (MFS) is critical in high-dimensional data analysis and multi-label learning, yet existing federated MFS methods often suffer from low solution quality, weak convergence, and limited privacy protection. To overcome these limitations, we propose Fed-MGACO, a novel federated framework that integrates manifold sparse constraints (MSC) and game-theoretic evolutionary ant colony optimization (GTEACO). In this framework, each client executes a two-stage MSC-GTEACO algorithm: in the first stage, MSC performs manifold preservation with sparse constraints to derive an initial feature weight matrix; in the second stage, GTEACO refines the solution through a heterogeneous ant colony, guided by game-theoretic pheromone updates, to capture cross-label dependencies and local distribution variations. Federated training employs a feature-weight–driven adaptive aggregation and distribution strategy, where clients share only their optimized feature-weight matrices with the server–while retaining raw data locally to preserve privacy–for secure aggregation. The aggregated results are then redistributed to clients to guide subsequent local refinements until convergence. Extensive evaluations on the CEC2022 benchmark functions and multiple real-world multi-label datasets show that Fed-MGACO delivers superior global optimization capability and faster convergence than baseline methods, while also yielding consistent improvements across key evaluation metrics, thereby demonstrating the effectiveness and robustness of the proposed method.

Low-altitude wind fields significantly increase the complexity of multi-UAV cooperative path planning (MUAVCP) by affecting flight time, energy consumption, trajectory feasibility, and arrival coordination. To address this problem, this study establishes a three-dimensional MUAVCP model considering kinematic constraints, no-fly zones, temporal coordination, inter-UAV separation, and multiple mission-related costs. An improved hybrid Rime–Snow Ablation Optimizer (IHRSO) is proposed. IHRSO employs the previously developed IRIME as the primary search component and a normal-cloud-model-enhanced SAOHTC (NCM-SAOHTC) as the auxiliary component. The two components are coordinated through an adapted stagnation-triggered mechanism that activates auxiliary search after insufficient primary-search improvement and subsequently returns the shared elite to IRIME for further refinement. IHRSO is evaluated on the CEC2017 and CEC2022 benchmark suites and several multi-UAV path-planning scenarios under low-altitude wind conditions. Ablation experiments show that IHRSO achieves the best Friedman mean rank of 1.375 and reduces optimization error by an average of 37.30% compared with fixed-period switching. In multi-UAV simulations, IHRSO consistently generates high-quality coordinated trajectories with lower mission delays and planning costs. In the large-fleet experiments, IHRSO maintains favorable solution quality and mission-delay performance as the fleet size increases to 20 UAVs, although the computational cost rises substantially with problem dimensionality.