Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process u...A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的...Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的3个周期性尖峰群、不同主机发送或接收流量的重尾分布、p2p流量的长相关特性以及提出了ADTE的估计方法来区分信令和数据流量。本文的研究也显示出Napster在p2p流中占大部分,这暗示着超级节点和阶层式拓扑较纯p2p结构潜在的优势。同时,观察到在我国p2p的流量仅占Internet总流量的1%弱,这个值跟国外的数据有很大区别。我们分析了其中的原因并希望该结论为我国p2p软件的发展提供参考。展开更多
以小世界模型为理论基础,以 Region 为基本逻辑管理单位,按用户需求和共享目的组织 Region。提出了基于 Region 的多层结构 Peer-to-Peer 网络模型和构造规则,给出了 Region 的划分策略和数学模型,证明了模型的正确和合理性;对模型中的...以小世界模型为理论基础,以 Region 为基本逻辑管理单位,按用户需求和共享目的组织 Region。提出了基于 Region 的多层结构 Peer-to-Peer 网络模型和构造规则,给出了 Region 的划分策略和数学模型,证明了模型的正确和合理性;对模型中的层和域、中心节点、普通节点和汇聚点进行了明确的定义,给出了节点加入、离开、中心节点选取策略和算法描述;使定位某种服务的工作量和查询范围从网络中的所有结点数降低到 Region 的节点数,有效地防止了恶意请求引发的洪,网络系统开销为常数。模拟分析表明,该模型可有效解决可扩展性、性能与效率不高问题,且网络规模越大,其综合性能的优越性越明显,因此,模型是合理有效的。展开更多
提出并实现了一种建立在Peer-to-Peer 搜索策略上的自组织、自适应、高效和可靠的文件系统DISPFS(Double ID Space basedPeer-to-peer File System)。它在双层ID 空间中构造虚拟存储节点,不仅有效地取得了文件系统内的负载均衡、提高系...提出并实现了一种建立在Peer-to-Peer 搜索策略上的自组织、自适应、高效和可靠的文件系统DISPFS(Double ID Space basedPeer-to-peer File System)。它在双层ID 空间中构造虚拟存储节点,不仅有效地取得了文件系统内的负载均衡、提高系统利用率,而且保证了动态环境中文件的可靠、快速获取。试验数据表明,DISPFS 在系统接近满负荷运行和文件插入/删除操作频繁的双重压力下依然保持优良的性能。展开更多
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900603)the National Natural Science Foundation of China(61831008).
文摘A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
文摘Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的3个周期性尖峰群、不同主机发送或接收流量的重尾分布、p2p流量的长相关特性以及提出了ADTE的估计方法来区分信令和数据流量。本文的研究也显示出Napster在p2p流中占大部分,这暗示着超级节点和阶层式拓扑较纯p2p结构潜在的优势。同时,观察到在我国p2p的流量仅占Internet总流量的1%弱,这个值跟国外的数据有很大区别。我们分析了其中的原因并希望该结论为我国p2p软件的发展提供参考。
文摘以小世界模型为理论基础,以 Region 为基本逻辑管理单位,按用户需求和共享目的组织 Region。提出了基于 Region 的多层结构 Peer-to-Peer 网络模型和构造规则,给出了 Region 的划分策略和数学模型,证明了模型的正确和合理性;对模型中的层和域、中心节点、普通节点和汇聚点进行了明确的定义,给出了节点加入、离开、中心节点选取策略和算法描述;使定位某种服务的工作量和查询范围从网络中的所有结点数降低到 Region 的节点数,有效地防止了恶意请求引发的洪,网络系统开销为常数。模拟分析表明,该模型可有效解决可扩展性、性能与效率不高问题,且网络规模越大,其综合性能的优越性越明显,因此,模型是合理有效的。
文摘提出并实现了一种建立在Peer-to-Peer 搜索策略上的自组织、自适应、高效和可靠的文件系统DISPFS(Double ID Space basedPeer-to-peer File System)。它在双层ID 空间中构造虚拟存储节点,不仅有效地取得了文件系统内的负载均衡、提高系统利用率,而且保证了动态环境中文件的可靠、快速获取。试验数据表明,DISPFS 在系统接近满负荷运行和文件插入/删除操作频繁的双重压力下依然保持优良的性能。
