无线通信与网络中的去中心化学习
Decentralized learning for wireless communications and networking
AI总结：
本文提出了一种去中心化学习算法，用于图数据的网络内处理，通过交替方向乘子法实现分布式优化，适用于无线通信和网络任务的案例研究。
摘要：
本章讨论了用于图数据网络内处理的去中心化学习算法。提出了一个通用学习问题，并将其转换为可分离形式，通过交替方向乘子法（ADMM）迭代最小化以实现所需程度的并行化。在不交换分布式训练集元素且保持节点间通信成本可控的情况下，本地学习器同意全球推断得到的量，即若整个训练数据集集中可用时所获得的量。通过包括使用无线传感器网络的目标跟踪、揭示互联网流量异常、电力系统状态估计以及无线认知无线电网络的频谱制图等案例研究，展示了去中心化学习框架对当代无线通信和网络任务的影响。
英文摘要：
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of parallelization. Without exchanging elements from the distributed training sets and keeping inter-node communications at affordable levels, the local (per-node) learners consent to the desired quantity inferred globally, meaning the one obtained if the entire training data set were centrally available. Impact of the decentralized learning framework to contemporary wireless communications and networking tasks is illustrated through case studies including target tracking using wireless sensor networks, unveiling Internet traffic anomalies, power system state estimation, as well as spectrum cartography for wireless cognitive radio networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
