隐私保护的分布式优化算法数据集

本数据集面向对群智系统分布式优化问题，基于数值仿真的方法，对所设计的基于ADMM的隐私保护分布式优化算法进行了测试。该算法采用一种状态扰动方法，重新设计了更新率，以实现对群智系统实时状态和成本函数的隐私信息保护。数据集记录一个5节点完全图下算法的运行数据与结果，包含了各个节点状态信息，成本函数值信息以及图矩阵信息等数据以及代码，数据大小为79.40KB。This dataset targets the distributed optimization problems of swarm intelligence systems, and tests the proposed ADMM-based privacy-preserving distributed optimization algorithm via numerical simulation methods. The algorithm adopts a state perturbation strategy and redesigns the update rate to safeguard the privacy of real-time states and cost functions of the swarm intelligence systems. The dataset records the operational data and results of the algorithm under a 5-node complete graph, including data such as the state information of each node, cost function value information, graph matrix information, as well as the supporting code. The total size of the dataset is 79.40 KB.

搜集汇总

背景与挑战

背景概述

该数据集基于数值仿真，面向群智系统分布式优化问题，测试了一种采用状态扰动方法的基于ADMM的隐私保护分布式优化算法。数据记录了5节点完全图下各节点状态、成本函数值及图矩阵等信息，包含代码，大小为79.40KB。