As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and effic...As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model,especially in partially homomorphic cryptosystems.In this paper,we propose a Practical and Efficient Privacy-preserving Federated Learning(PEPFL)framework.First,we present a lifted distributed ElGamal cryptosystem for federated learning,which can solve the multi-key problem in federated learning.Secondly,we develop a Practical Partially Single Instruction Multiple Data(PSIMD)parallelism scheme that can encode a plaintext matrix into single plaintext for encryption,improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem.In addition,based on the Convolutional Neural Network(CNN)and the designed cryptosystem,a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent(MGD).Finally,we evaluate the security and performance of PEPFL.The experiment results demonstrate that the scheme is practicable,effective,and secure with low communication and computation costs.展开更多
Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, para...Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.展开更多
离群点检测是数据管理领域中的一个重要问题,在信用卡欺诈检测、工业工程过程管理、银行数据处理等方面等均有广泛应用.大数据时代的到来加剧了对大规模流媒体数据进行离群点检测多样化的需求,不同用户可根据自身偏好选择不同类型的数...离群点检测是数据管理领域中的一个重要问题,在信用卡欺诈检测、工业工程过程管理、银行数据处理等方面等均有广泛应用.大数据时代的到来加剧了对大规模流媒体数据进行离群点检测多样化的需求,不同用户可根据自身偏好选择不同类型的数据作为离群点.针对流数据环境下多离群点检测问题,提出了一种查询处理框架MQOD(Multiple Query of Outlier Detection),利用多查询任务之间的包含关系来支持多离群点检测任务,从而提高查询效率.在MQOD框架下,构建了HT-Grid索引以支持流数据的管理,利用滑动窗口的时间特性对窗口进行划分,并根据划分结果确定执行查询的范围,减少不必要的对象访问.通过真实数据集和合成数据集对MQOD算法进行了验证,验证结果表征了算法的高效性.展开更多
A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set process...A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set processor(ASIP), which uses TSE algorithm instead of resource-consuming reciprocal and reciprocal square root(RSR) operations.The aim is to give a high performance implementation for MMSE and QRD in one programmable platform simultaneously.Furthermore, instruction set architecture(ISA) and the allocation of data paths in single instruction multiple data-very long instruction word(SIMD-VLIW) architecture are provided, offering more data parallelism and instruction parallelism for different dimension matrices and operation types.Meanwhile, multiple level numerical precision can be achieved with flexible table size and expansion order in TSE ISA.The ASIP has been implemented to a 28 nm CMOS process and frequency reaches 800 MHz.Experimental results show that the proposed design provides perfect numerical precision within the fixed bit-width of the ASIP, higher matrix processing rate better than the requirements of 5G system and more rate-area efficiency comparable with ASIC implementations.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.U19B2021the Key Research and Development Program of Shaanxi under Grant No.2020ZDLGY08-04+1 种基金the Key Technologies R&D Program of He’nan Province under Grant No.212102210084the Innovation Scientists and Technicians Troop Construction Projects of Henan Province.
文摘As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model,especially in partially homomorphic cryptosystems.In this paper,we propose a Practical and Efficient Privacy-preserving Federated Learning(PEPFL)framework.First,we present a lifted distributed ElGamal cryptosystem for federated learning,which can solve the multi-key problem in federated learning.Secondly,we develop a Practical Partially Single Instruction Multiple Data(PSIMD)parallelism scheme that can encode a plaintext matrix into single plaintext for encryption,improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem.In addition,based on the Convolutional Neural Network(CNN)and the designed cryptosystem,a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent(MGD).Finally,we evaluate the security and performance of PEPFL.The experiment results demonstrate that the scheme is practicable,effective,and secure with low communication and computation costs.
文摘Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.
文摘离群点检测是数据管理领域中的一个重要问题,在信用卡欺诈检测、工业工程过程管理、银行数据处理等方面等均有广泛应用.大数据时代的到来加剧了对大规模流媒体数据进行离群点检测多样化的需求,不同用户可根据自身偏好选择不同类型的数据作为离群点.针对流数据环境下多离群点检测问题,提出了一种查询处理框架MQOD(Multiple Query of Outlier Detection),利用多查询任务之间的包含关系来支持多离群点检测任务,从而提高查询效率.在MQOD框架下,构建了HT-Grid索引以支持流数据的管理,利用滑动窗口的时间特性对窗口进行划分,并根据划分结果确定执行查询的范围,减少不必要的对象访问.通过真实数据集和合成数据集对MQOD算法进行了验证,验证结果表征了算法的高效性.
基金Supported by the Industrial Internet Innovation and Development Project of Ministry of Industry and Information Technology (No.GHBJ2004)。
文摘A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set processor(ASIP), which uses TSE algorithm instead of resource-consuming reciprocal and reciprocal square root(RSR) operations.The aim is to give a high performance implementation for MMSE and QRD in one programmable platform simultaneously.Furthermore, instruction set architecture(ISA) and the allocation of data paths in single instruction multiple data-very long instruction word(SIMD-VLIW) architecture are provided, offering more data parallelism and instruction parallelism for different dimension matrices and operation types.Meanwhile, multiple level numerical precision can be achieved with flexible table size and expansion order in TSE ISA.The ASIP has been implemented to a 28 nm CMOS process and frequency reaches 800 MHz.Experimental results show that the proposed design provides perfect numerical precision within the fixed bit-width of the ASIP, higher matrix processing rate better than the requirements of 5G system and more rate-area efficiency comparable with ASIC implementations.
