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 chal...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 L2,1-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.展开更多
Developing an efficient freshwater and electricity co-generation device(FECGD)can solve the shortage of freshwater and electricity.However,the poor salt resistance and refrigeration properties of the materials for FEC...Developing an efficient freshwater and electricity co-generation device(FECGD)can solve the shortage of freshwater and electricity.However,the poor salt resistance and refrigeration properties of the materials for FECGD put big challenges in the efficient and stable operation of these devices.To address these issues,we propose the covalent organic framework(COF)confined co-polymerization strategy to prepare COF-modified acrylamide cationic hydrogels(ACH-COF),where hydrogen bonding interlocking between negatively charged polymer chains and COF pores can form a salt resistant hydrogel for stabilizing tunable passive interfacial cooling(TPIC).The FECPDs based on the TPIC and salt resistance of ACH-COF display a maximum output power density of 2.28 W m-2,which is 4.3 times higher than that of a commercial thermoelec-tric generator under one solar radiation.The production rate of freshwater can reach 2.74 kg m-2 h-1.Our results suggest that the high efficiency and scala-bility of the FECGD can hold the promise of alleviating freshwater and power shortages.展开更多
文摘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 L2,1-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.
基金National Natural Science Foundation of China,Grant/Award Numbers:22108125,22175094Natural Science Foundation of Jiangsu Province,Grant/Award Number:BK20210627+1 种基金China Postdoctoral Science Foundation,Grant/Award Number:2023M730484Postgraduate Research&Practice Innovation Program of Jiangsu Province,Grant/Award Number:KYCX23_1173。
文摘Developing an efficient freshwater and electricity co-generation device(FECGD)can solve the shortage of freshwater and electricity.However,the poor salt resistance and refrigeration properties of the materials for FECGD put big challenges in the efficient and stable operation of these devices.To address these issues,we propose the covalent organic framework(COF)confined co-polymerization strategy to prepare COF-modified acrylamide cationic hydrogels(ACH-COF),where hydrogen bonding interlocking between negatively charged polymer chains and COF pores can form a salt resistant hydrogel for stabilizing tunable passive interfacial cooling(TPIC).The FECPDs based on the TPIC and salt resistance of ACH-COF display a maximum output power density of 2.28 W m-2,which is 4.3 times higher than that of a commercial thermoelec-tric generator under one solar radiation.The production rate of freshwater can reach 2.74 kg m-2 h-1.Our results suggest that the high efficiency and scala-bility of the FECGD can hold the promise of alleviating freshwater and power shortages.
