“Client/Server数据库”一词很容易使人联想到那种大量重任在肩的用户使用的、高性能的关系数据库产品。这种想法也许要改变了。现在,Claris公司向FileMaker Pro的用户提供了一种更为轻松的Client/Server计算模式:一种便于使用的、非...“Client/Server数据库”一词很容易使人联想到那种大量重任在肩的用户使用的、高性能的关系数据库产品。这种想法也许要改变了。现在,Claris公司向FileMaker Pro的用户提供了一种更为轻松的Client/Server计算模式:一种便于使用的、非关系型数据库产品FileMaker Pro 3.0Server for Windows NT。 FileMaker Pro 3.0 Server软件来自于目前正在使用的Macintosh版本,可运行于Windows NT 3.51或更新的版本上。与Macintosh版本相同,它扩展了FileMaker Pro对多用户的支持能力:支持最多100个并发用户和最多可打开100个文件。展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
文摘“Client/Server数据库”一词很容易使人联想到那种大量重任在肩的用户使用的、高性能的关系数据库产品。这种想法也许要改变了。现在,Claris公司向FileMaker Pro的用户提供了一种更为轻松的Client/Server计算模式:一种便于使用的、非关系型数据库产品FileMaker Pro 3.0Server for Windows NT。 FileMaker Pro 3.0 Server软件来自于目前正在使用的Macintosh版本,可运行于Windows NT 3.51或更新的版本上。与Macintosh版本相同,它扩展了FileMaker Pro对多用户的支持能力:支持最多100个并发用户和最多可打开100个文件。
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
