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- W4360764495 abstract "The distributed alternating direction method of mul-tipliers (ADMM) is an effective algorithm to solve large-scale op-timization problems. However, there are still massive computation and communication cost in distributed ADMM when processing high-dimensional data. To solve this problem, we propose a distributed ADMM with sparse computation and Allreduce communication (SCAC-ADMM) which can process high-dimensional data effectively. In the algorithm, each node optimizes a sub-model of the target model in parallel. Then, the target model is obtained by aggregating all sub-models. The features in the sub-model are named associated features. In SCAC-ADMM, we first design a selecting method of associated features to determine the composition of each sub-model. This method can limit the dimension of the sub-model by setting appropriate parameters, so as to limit the computation cost. Secondly, to reduce the communication traffic caused by transmitting high-dimensional parameters, we propose a novel Allreduce communication model which can only aggregate associated parameters in sub-models. Experiments on high-dimensional datasets show that SCAC-ADMM has less computation cost and higher communication efficiency than traditional distributed ADMM. When solving large-scale logistic regression problem, SCAC-ADMM can reduce the system time by 73% compared with traditional distributed ADMM." @default.
- W4360764495 created "2023-03-25" @default.
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- W4360764495 date "2022-12-01" @default.
- W4360764495 modified "2023-09-27" @default.
- W4360764495 title "Distributed ADMM Based on Sparse Computation and Allreduce Communication" @default.
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- W4360764495 doi "https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00098" @default.
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