Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequen...In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequence of L^(1)functions converging to the given function and verifying their representation in the form of Fourier transform to establish the desired result of the given function.Applying this main result,we further generalize the Paley-Wiener theorem for band-limited functions to the analytic function spaces L^(p)(0<p<∞)with general weights.展开更多
Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu...Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.展开更多
In this paper, we give the four equivalent characterizations for the weighted local hardy spaces on Lipschitz domains. Also, we give their application for the harmonic function defined in bounded Lipschitz domains.
We study the bounded and the compact weighted composition operators from the Bloch space into the weighted Banach spaces of holomorphic functions on bounded homogeneous domains, with particular attention to the unit p...We study the bounded and the compact weighted composition operators from the Bloch space into the weighted Banach spaces of holomorphic functions on bounded homogeneous domains, with particular attention to the unit polydisk. For bounded homogeneous domains, we characterize the bounded weighted composition operators and determine the operator norm. In addition, we provide sufficient conditions for compactness. For the unit polydisk, we completely characterize the compact weighted composition operators, as well as provide ＂computable＂ estimates on the operator norm.展开更多
On bounded symmetric domain Ω of C^n, we investigate the properties of functions in weighted Bergman spaces A^P（Ω,dvs） for 0 〈 p ≤ ＋∞ and -1 〈 s 〈 4-∞. Based on the estimate of Bergman kernel, we obtain som...On bounded symmetric domain Ω of C^n, we investigate the properties of functions in weighted Bergman spaces A^P（Ω,dvs） for 0 〈 p ≤ ＋∞ and -1 〈 s 〈 4-∞. Based on the estimate of Bergman kernel, we obtain some characterizations of functions in A^P（Ω, dvs） in terms of a class of linear operators D^αB. Making use of these characterizations, we extend A^P（Ω,dvs） to the weighted Bergman spaces Aα^p,B（Ω,dvs） in a very natural way for 1 〈 p 〈 4-∞ and any real number s, that is, -∞ 〈 s 〈 ＋∞. This unified treatment covers some classical Bergman spaces, Besov spaces and Bloch spaces. Meanwhile, the boundedness of Bergman projection operators on Aα^P,β（Ω, dvs） and the dual of Aα^P,B（Ω, dvs） are given.展开更多
Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-...Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.展开更多
Let {Xn, n ≥ 1} be a sequence of independent and identically distributed positive valued random variables with a common distribution function F. When F belongs to the domain of partial attraction of a semi stable law...Let {Xn, n ≥ 1} be a sequence of independent and identically distributed positive valued random variables with a common distribution function F. When F belongs to the domain of partial attraction of a semi stable law with index α, 0 < α < 1, an asymptotic behavior of the large deviation probabilities with respect to properly normalized weighted sums have been studied and in support of this we obtained Chover’s form of law of iterated logarithm.展开更多
Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and...Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and quantity of the sources are commonly unknown.Existing multi-source search methods fail to accurately estimate the source term,primarily due to the inefficient utilization of concentration information.This limitation results in sub-optimal drone movement strategies.To address these issues,we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis(DLW-CI)approach.The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism.Specifically,we devise a novel source term estimation method that leverages multiple parallel particle filters,with each filter estimating the parameters of a potentially unknown source in scenarios.Subsequently,we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source.The results show that the success rate for the localization of 2-4 diffusion sources reaches 90%,78%,and 42% respectively when employing the DLW-CI approach,achieving a 37%average improvement over baseline methods.Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search,making a valuable contribution to environmental safety monitoring applications.展开更多
【目的】工业控制系统(industrial control system,ICS)中设备间通信过程高度依赖工控协议来实现,协议安全性对保障ICS稳定运行起到关键作用。漏洞挖掘与入侵检测等作为ICS安全防御体系的核心技术组件,其有效性依赖于对工控协议结构及...【目的】工业控制系统(industrial control system,ICS)中设备间通信过程高度依赖工控协议来实现,协议安全性对保障ICS稳定运行起到关键作用。漏洞挖掘与入侵检测等作为ICS安全防御体系的核心技术组件,其有效性依赖于对工控协议结构及语义功能的精确解析。协议逆向分析作为解析协议结构与语义功能的关键技术,其核心环节语义推断精度直接决定协议理解的准确性。然而,受限于工控协议文档缺失、格式异构性强等现实条件,现有语义推断方法普遍依赖专家经验,存在自动化水平不足、跨协议泛化性能有限等固有瓶颈,难以适应实际工业环境中多源异构协议的高精度解析需求。【方法】为解决上述问题,本文提出mBERT协同多源领域自适应与结构化掩码策略的语义推断方法。通过mBERT模型实现跨协议通用语义表示;利用结合注意力权重与位置编码设计的结构化掩码策略,增强模型对协议结构和语义内在联系的表示能力,提高语义推断方法的自动化程度和效率;利用结合对抗训练的多源领域自适应逐步微调策略,提升模型对多个源协议的语义通用表示能力,增强其在多种工控协议上的适用性,实现关键字语义的有效推断。【结果】在辽宁省石油化工行业信息安全重点实验室的典型能源企业攻防演练靶场中开展实验验证,采集了S7comm、Modbus/TCP和EtherNet/IP三种工控协议数据,并利用协议复杂度评分机制组建训练数据集。结果表明,多源领域自适应逐步微调策略能够显著提升模型性能,将其与结构化掩码策略结合,进一步提高了语义推断精度,且本文方法在精确度、召回率与F_(1)分数指标上均显著优于现有基线方法。【结论】本文提出了mBERT协同多源领域自适应与结构化掩码策略的语义推断方法,在语义推断中采用高维球面映射与多任务损失函数,增强了不同语义类别的区分度与模型对协议语义的深层辨识能力。本文方法不仅显著降低了对人工先验知识的依赖,也提升了语义推断效率与跨协议适用性,为工控协议逆向分析及工业系统安全防护提供了具备理论支撑的新路径。展开更多
This paper is devoted to estimates on weighted L^(q)-norms of the nonstationary 3D Navier-Stokes flow in an exterior domain.By multiplying the Navier-Stokes equation with a well selected vector field,an integral equat...This paper is devoted to estimates on weighted L^(q)-norms of the nonstationary 3D Navier-Stokes flow in an exterior domain.By multiplying the Navier-Stokes equation with a well selected vector field,an integral equation is derived,from which,w etablish the eight etmate‖|x|^(α)u(t)‖q≤(1+t^(α/2+ε))t^−3/2(1-1/q),t>0, where 0<α≤1 and 3/2<q<∞,or 1<α<2 and 3/3-α<q<∞,0<ε<1 is arbitrary,and μ_(0)∈L_(σ)^(3)(Ω),|x|^(α)u(0)∈L^(1)(Ω) with ‖μ_(0)‖_(3) sufficiently small.With the aid of the representation of the flow,we also prove that if in addition μ_(0)∈D_(a)^(1-1/b,b) for some 6/5≤α<3/2 and 1<b<2 with 3/a+2/b=4,then the ptimal estimate ‖|x|^(α)u(t)‖q≤C(1+t^(α/2))t^(-3/2(1-1/q)),t>0 holds,where α>0 and 1<q<∞.Compared with the literature,here no extra restriction is laid on the range of the exponents α and q.展开更多
By the interpolating inequality and a priori estimates in the weighted space,the existence of the global solutions for the Ginzburg-Landau equation coupled with the BBM equation in an unbounded domain is considered, a...By the interpolating inequality and a priori estimates in the weighted space,the existence of the global solutions for the Ginzburg-Landau equation coupled with the BBM equation in an unbounded domain is considered, and the existence of the maximal attractor is obtained.展开更多
Smart manufacturing suffers from the heterogeneity of local data distribution across parties,mutual information silos and lack of privacy protection in the process of industry chain collaboration.To address these prob...Smart manufacturing suffers from the heterogeneity of local data distribution across parties,mutual information silos and lack of privacy protection in the process of industry chain collaboration.To address these problems,we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning.Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain.A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model,while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum,mitigating the inherent heterogeneity between local data.Our experiments are conducted on the largest domain adaptation dataset,and the results show that compared with other traditional federated domain adaptation algorithms,the algorithm we proposed trains a more accurate model,requires fewer communication rounds,makes more effective use of imbalanced data in the industrial area,and protects data privacy.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金Supported by the National Natural Science Foundation of China(12301101)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110019 and 2020A1515110585)。
文摘In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequence of L^(1)functions converging to the given function and verifying their representation in the form of Fourier transform to establish the desired result of the given function.Applying this main result,we further generalize the Paley-Wiener theorem for band-limited functions to the analytic function spaces L^(p)(0<p<∞)with general weights.
基金This study was supported by National Key Research and Development Project(Project No.2017YFD0301506)National Social Science Foundation(Project No.71774052)+1 种基金Hunan Education Department Scientific Research Project(Project No.17K04417A092).
文摘Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
基金Project supported by the National Natural Science Foundation of China (No. 10377108)the Natural Science Foundation of Guangdong Province (No. 031495), China
文摘In this paper, we give the four equivalent characterizations for the weighted local hardy spaces on Lipschitz domains. Also, we give their application for the harmonic function defined in bounded Lipschitz domains.
文摘We study the bounded and the compact weighted composition operators from the Bloch space into the weighted Banach spaces of holomorphic functions on bounded homogeneous domains, with particular attention to the unit polydisk. For bounded homogeneous domains, we characterize the bounded weighted composition operators and determine the operator norm. In addition, we provide sufficient conditions for compactness. For the unit polydisk, we completely characterize the compact weighted composition operators, as well as provide ＂computable＂ estimates on the operator norm.
基金the NNSF of China(10571164)the SRFDP of Higher Education(20050358052)
文摘On bounded symmetric domain Ω of C^n, we investigate the properties of functions in weighted Bergman spaces A^P（Ω,dvs） for 0 〈 p ≤ ＋∞ and -1 〈 s 〈 4-∞. Based on the estimate of Bergman kernel, we obtain some characterizations of functions in A^P（Ω, dvs） in terms of a class of linear operators D^αB. Making use of these characterizations, we extend A^P（Ω,dvs） to the weighted Bergman spaces Aα^p,B（Ω,dvs） in a very natural way for 1 〈 p 〈 4-∞ and any real number s, that is, -∞ 〈 s 〈 ＋∞. This unified treatment covers some classical Bergman spaces, Besov spaces and Bloch spaces. Meanwhile, the boundedness of Bergman projection operators on Aα^P,β（Ω, dvs） and the dual of Aα^P,B（Ω, dvs） are given.
文摘Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.
文摘Let {Xn, n ≥ 1} be a sequence of independent and identically distributed positive valued random variables with a common distribution function F. When F belongs to the domain of partial attraction of a semi stable law with index α, 0 < α < 1, an asymptotic behavior of the large deviation probabilities with respect to properly normalized weighted sums have been studied and in support of this we obtained Chover’s form of law of iterated logarithm.
基金supported by the National Natural Science Foundation of China 62173337Youth Independent Innovation Foundation of NUDT(ZK-2023-21).
文摘Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and quantity of the sources are commonly unknown.Existing multi-source search methods fail to accurately estimate the source term,primarily due to the inefficient utilization of concentration information.This limitation results in sub-optimal drone movement strategies.To address these issues,we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis(DLW-CI)approach.The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism.Specifically,we devise a novel source term estimation method that leverages multiple parallel particle filters,with each filter estimating the parameters of a potentially unknown source in scenarios.Subsequently,we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source.The results show that the success rate for the localization of 2-4 diffusion sources reaches 90%,78%,and 42% respectively when employing the DLW-CI approach,achieving a 37%average improvement over baseline methods.Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search,making a valuable contribution to environmental safety monitoring applications.
文摘【目的】工业控制系统(industrial control system,ICS)中设备间通信过程高度依赖工控协议来实现,协议安全性对保障ICS稳定运行起到关键作用。漏洞挖掘与入侵检测等作为ICS安全防御体系的核心技术组件,其有效性依赖于对工控协议结构及语义功能的精确解析。协议逆向分析作为解析协议结构与语义功能的关键技术,其核心环节语义推断精度直接决定协议理解的准确性。然而,受限于工控协议文档缺失、格式异构性强等现实条件,现有语义推断方法普遍依赖专家经验,存在自动化水平不足、跨协议泛化性能有限等固有瓶颈,难以适应实际工业环境中多源异构协议的高精度解析需求。【方法】为解决上述问题,本文提出mBERT协同多源领域自适应与结构化掩码策略的语义推断方法。通过mBERT模型实现跨协议通用语义表示;利用结合注意力权重与位置编码设计的结构化掩码策略,增强模型对协议结构和语义内在联系的表示能力,提高语义推断方法的自动化程度和效率;利用结合对抗训练的多源领域自适应逐步微调策略,提升模型对多个源协议的语义通用表示能力,增强其在多种工控协议上的适用性,实现关键字语义的有效推断。【结果】在辽宁省石油化工行业信息安全重点实验室的典型能源企业攻防演练靶场中开展实验验证,采集了S7comm、Modbus/TCP和EtherNet/IP三种工控协议数据,并利用协议复杂度评分机制组建训练数据集。结果表明,多源领域自适应逐步微调策略能够显著提升模型性能,将其与结构化掩码策略结合,进一步提高了语义推断精度,且本文方法在精确度、召回率与F_(1)分数指标上均显著优于现有基线方法。【结论】本文提出了mBERT协同多源领域自适应与结构化掩码策略的语义推断方法,在语义推断中采用高维球面映射与多任务损失函数,增强了不同语义类别的区分度与模型对协议语义的深层辨识能力。本文方法不仅显著降低了对人工先验知识的依赖,也提升了语义推断效率与跨协议适用性,为工控协议逆向分析及工业系统安全防护提供了具备理论支撑的新路径。
文摘This paper is devoted to estimates on weighted L^(q)-norms of the nonstationary 3D Navier-Stokes flow in an exterior domain.By multiplying the Navier-Stokes equation with a well selected vector field,an integral equation is derived,from which,w etablish the eight etmate‖|x|^(α)u(t)‖q≤(1+t^(α/2+ε))t^−3/2(1-1/q),t>0, where 0<α≤1 and 3/2<q<∞,or 1<α<2 and 3/3-α<q<∞,0<ε<1 is arbitrary,and μ_(0)∈L_(σ)^(3)(Ω),|x|^(α)u(0)∈L^(1)(Ω) with ‖μ_(0)‖_(3) sufficiently small.With the aid of the representation of the flow,we also prove that if in addition μ_(0)∈D_(a)^(1-1/b,b) for some 6/5≤α<3/2 and 1<b<2 with 3/a+2/b=4,then the ptimal estimate ‖|x|^(α)u(t)‖q≤C(1+t^(α/2))t^(-3/2(1-1/q)),t>0 holds,where α>0 and 1<q<∞.Compared with the literature,here no extra restriction is laid on the range of the exponents α and q.
文摘By the interpolating inequality and a priori estimates in the weighted space,the existence of the global solutions for the Ginzburg-Landau equation coupled with the BBM equation in an unbounded domain is considered, and the existence of the maximal attractor is obtained.
基金Supported by the Scientific and Technological Innovation 2030—Major Project of"New Generation Artificial Intelligence"(2020AAA0109300)。
文摘Smart manufacturing suffers from the heterogeneity of local data distribution across parties,mutual information silos and lack of privacy protection in the process of industry chain collaboration.To address these problems,we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning.Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain.A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model,while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum,mitigating the inherent heterogeneity between local data.Our experiments are conducted on the largest domain adaptation dataset,and the results show that compared with other traditional federated domain adaptation algorithms,the algorithm we proposed trains a more accurate model,requires fewer communication rounds,makes more effective use of imbalanced data in the industrial area,and protects data privacy.
