The Experts below are selected from a list of 67833 Experts worldwide ranked by ideXlab platform
Salman A. Avestimehr - One of the best experts on this subject based on the ideXlab platform.
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Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation
2019 IEEE International Symposium on Information Theory (ISIT), 2019Co-Authors: Qian Yu, Salman A. AvestimehrAbstract:We consider the problem of distributedly computing a general class of functions, referred to as gradient-Type Computation, while maintaining the privacy of the input dataset. Gradient-Type Computation evaluates the sum of some "partial gradients", defined as polynomials of subsets of the input. It underlies many algorithms in machine learning and data analytics. We propose Harmonic Coding, which universally computes any gradient-Type function, while requiring the minimum possible number of workers. Harmonic Coding strictly improves computing schemes developed based on prior works, such as Shamir's secret sharing and Lagrange Coded Computing, by injecting coded redundancy using harmonic progression. It enables the computing results of the workers to be interpreted as the sum of partial gradients and some redundant results, which then allows the cancellation of non-gradient terms in the decoding process. By proving a matching converse, we demonstrate the optimality of Harmonic Coding, even compared to the schemes that are non-universal (i.e., can be designed based on a specific gradient-Type function).
Qian Yu - One of the best experts on this subject based on the ideXlab platform.
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Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation
2019 IEEE International Symposium on Information Theory (ISIT), 2019Co-Authors: Qian Yu, Salman A. AvestimehrAbstract:We consider the problem of distributedly computing a general class of functions, referred to as gradient-Type Computation, while maintaining the privacy of the input dataset. Gradient-Type Computation evaluates the sum of some "partial gradients", defined as polynomials of subsets of the input. It underlies many algorithms in machine learning and data analytics. We propose Harmonic Coding, which universally computes any gradient-Type function, while requiring the minimum possible number of workers. Harmonic Coding strictly improves computing schemes developed based on prior works, such as Shamir's secret sharing and Lagrange Coded Computing, by injecting coded redundancy using harmonic progression. It enables the computing results of the workers to be interpreted as the sum of partial gradients and some redundant results, which then allows the cancellation of non-gradient terms in the decoding process. By proving a matching converse, we demonstrate the optimality of Harmonic Coding, even compared to the schemes that are non-universal (i.e., can be designed based on a specific gradient-Type function).
Zhaoyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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delay constrained hybrid Computation offloading with cloud and fog computing
IEEE Access, 2017Co-Authors: Xianling Meng, Wei Wang, Zhaoyang ZhangAbstract:To satisfy the delay constraint, the Computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single Type of offloading destinations, in this paper, we study the hybrid Computation offloading problem considering diverse Computation and communication capabilities of two Types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and Computation while completing the Computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-Computation scheduling problem for the Computation offloading to different destinations. To solve the Computation offloading problem, we first define a new concept named Computation energy efficiency and divide the problem into four subproblems according to the Computation energy efficiency of different Types of Computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form Computation offloading solution with the analysis of communication-Computation scheduling under the delay constraint. The numerical results show that the proposed hybrid Computation offloading solution achieves lower energy consumption than the conventional single-Type Computation offloading under the delay constraint.
Xianling Meng - One of the best experts on this subject based on the ideXlab platform.
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delay constrained hybrid Computation offloading with cloud and fog computing
IEEE Access, 2017Co-Authors: Xianling Meng, Wei Wang, Zhaoyang ZhangAbstract:To satisfy the delay constraint, the Computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single Type of offloading destinations, in this paper, we study the hybrid Computation offloading problem considering diverse Computation and communication capabilities of two Types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and Computation while completing the Computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-Computation scheduling problem for the Computation offloading to different destinations. To solve the Computation offloading problem, we first define a new concept named Computation energy efficiency and divide the problem into four subproblems according to the Computation energy efficiency of different Types of Computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form Computation offloading solution with the analysis of communication-Computation scheduling under the delay constraint. The numerical results show that the proposed hybrid Computation offloading solution achieves lower energy consumption than the conventional single-Type Computation offloading under the delay constraint.
Wei Wang - One of the best experts on this subject based on the ideXlab platform.
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delay constrained hybrid Computation offloading with cloud and fog computing
IEEE Access, 2017Co-Authors: Xianling Meng, Wei Wang, Zhaoyang ZhangAbstract:To satisfy the delay constraint, the Computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single Type of offloading destinations, in this paper, we study the hybrid Computation offloading problem considering diverse Computation and communication capabilities of two Types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and Computation while completing the Computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-Computation scheduling problem for the Computation offloading to different destinations. To solve the Computation offloading problem, we first define a new concept named Computation energy efficiency and divide the problem into four subproblems according to the Computation energy efficiency of different Types of Computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form Computation offloading solution with the analysis of communication-Computation scheduling under the delay constraint. The numerical results show that the proposed hybrid Computation offloading solution achieves lower energy consumption than the conventional single-Type Computation offloading under the delay constraint.
