以夏秋季极端高温山林火灾扑救行动为研究对象,以分布式机器学习为理论基础对任务中气温及机动兵力的建模和预测进行研究。首先提出一种基于联邦平均算法(Federal Average Algorithm,FedAvg)的模型构建方法,从更贴近任务实际、更加精细...以夏秋季极端高温山林火灾扑救行动为研究对象,以分布式机器学习为理论基础对任务中气温及机动兵力的建模和预测进行研究。首先提出一种基于联邦平均算法(Federal Average Algorithm,FedAvg)的模型构建方法,从更贴近任务实际、更加精细的角度对各任务方向的最高气温及机动兵力数量进行定量预测;其次通过引接政府公共资源平台及作战数据库中多区域气温和机动兵力,在各数据客户端不互传数据的情况下,通过聚合不同客户端参数共同训练全局模型达到预测目的,为各数据源无法共享环境下分析数据、使用数据提供理论支撑。展开更多
The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizat...The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.展开更多
作为一种分布式训练框架,联邦学习在无线通信领域有着广阔的应用前景,也面临着多方面的技术挑战,其中之一源于参与训练用户数据集的非独立同分布(Independent and identically distributed,IID)。不少文献提出了解决方法,以减轻户数据集...作为一种分布式训练框架,联邦学习在无线通信领域有着广阔的应用前景,也面临着多方面的技术挑战,其中之一源于参与训练用户数据集的非独立同分布(Independent and identically distributed,IID)。不少文献提出了解决方法,以减轻户数据集非IID造成的联邦学习性能损失。本文以平均信道增益预测、正交幅度调制信号的解调这两个无线任务以及两个图像分类任务为例,分析用户数据集非IID对联邦学习性能的影响,通过神经网络损失函数的可视化和对模型参数的偏移量进行分析,尝试解释非IID数据集对不同任务影响程度不同的原因。分析结果表明,用户数据集非IID未必导致联邦学习性能的下降。在不同数据集上通过联邦平均算法训练得到的模型参数偏移程度和损失函数形状有很大的差异,二者共同导致了不同任务受数据非IID影响程度的不同;在同一个回归问题中,数据集非IID是否影响联邦学习的性能与引起数据非IID的具体因素有关。展开更多
Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for ...Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training process.To remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new tasks.Of the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.展开更多
This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two yea...This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two years, as recommended by the State Environmental Institute of the state of Rio de Janeiro. The case study of the Rio de Janeiro section of the Paraiba do Sul River was investigated, and the results indicate that the Federal Heritage Department’s method does not consider the ongoing anthropization of the river, caused mainly by the construction and operation of hydroelectric plants. In addition, it was observed that the limnimetric scales of the studied gauging stations are influenced by constant changes in the riverbed and by riverbank occupation, making it difficult to estimate the ordinary flood level. The study concludes by suggesting the adoption of a flow rate with a recurrence interval of two years and the simulation of the runoff conditions for demarcation of the average ordinary flood line.展开更多
文摘以夏秋季极端高温山林火灾扑救行动为研究对象,以分布式机器学习为理论基础对任务中气温及机动兵力的建模和预测进行研究。首先提出一种基于联邦平均算法(Federal Average Algorithm,FedAvg)的模型构建方法,从更贴近任务实际、更加精细的角度对各任务方向的最高气温及机动兵力数量进行定量预测;其次通过引接政府公共资源平台及作战数据库中多区域气温和机动兵力,在各数据客户端不互传数据的情况下,通过聚合不同客户端参数共同训练全局模型达到预测目的,为各数据源无法共享环境下分析数据、使用数据提供理论支撑。
文摘The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.
文摘作为一种分布式训练框架,联邦学习在无线通信领域有着广阔的应用前景,也面临着多方面的技术挑战,其中之一源于参与训练用户数据集的非独立同分布(Independent and identically distributed,IID)。不少文献提出了解决方法,以减轻户数据集非IID造成的联邦学习性能损失。本文以平均信道增益预测、正交幅度调制信号的解调这两个无线任务以及两个图像分类任务为例,分析用户数据集非IID对联邦学习性能的影响,通过神经网络损失函数的可视化和对模型参数的偏移量进行分析,尝试解释非IID数据集对不同任务影响程度不同的原因。分析结果表明,用户数据集非IID未必导致联邦学习性能的下降。在不同数据集上通过联邦平均算法训练得到的模型参数偏移程度和损失函数形状有很大的差异,二者共同导致了不同任务受数据非IID影响程度的不同;在同一个回归问题中,数据集非IID是否影响联邦学习的性能与引起数据非IID的具体因素有关。
基金Supported by the Lazio region,in the scope of the project FedMedAI,Regional Operative Prgramme (POR) of the European fund for regional development (FESR) Lazio 2014–2020 (Azione 1.2.1)(No.A0375-2020-36491-23/10/2020)
文摘Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training process.To remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new tasks.Of the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.
文摘This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two years, as recommended by the State Environmental Institute of the state of Rio de Janeiro. The case study of the Rio de Janeiro section of the Paraiba do Sul River was investigated, and the results indicate that the Federal Heritage Department’s method does not consider the ongoing anthropization of the river, caused mainly by the construction and operation of hydroelectric plants. In addition, it was observed that the limnimetric scales of the studied gauging stations are influenced by constant changes in the riverbed and by riverbank occupation, making it difficult to estimate the ordinary flood level. The study concludes by suggesting the adoption of a flow rate with a recurrence interval of two years and the simulation of the runoff conditions for demarcation of the average ordinary flood line.
