论文标题
基于格拉米亚的基于网络扩散学习的自适应组合政策
Gramian-Based Adaptive Combination Policies for Diffusion Learning over Networks
论文作者
论文摘要
本文提出了通过扩散网络分布式学习的自适应组合策略。由于学习依赖于分散代理的随机信息的协作处理，因此可以通过设计根据数据质量来调整权重的组合策略来改进整体性能。此类政策很重要，因为它们会增加新的自由度和endow多代理系统，并能够控制信息在其边缘上的流动以增强性能。文献中可用的大多数自适应和静态策略优化了与稳态行为相关的某些性能指标，从而损害了瞬态行为。相比之下，我们制定了一个自适应组合规则，旨在优化瞬态学习绩效，同时保持使用先前在文献中制定的策略获得的增强的稳态绩效。
This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic information at the dispersed agents, the overall performance can be improved by designing combination policies that adjust the weights according to the quality of the data. Such policies are important because they would add a new degree of freedom and endow multi-agent systems with the ability to control the flow of information over their edges for enhanced performance. Most adaptive and static policies available in the literature optimize certain performance metrics related to steady-state behavior, to the detriment of transient behavior. In contrast, we develop an adaptive combination rule that aims at optimizing the transient learning performance, while maintaining the enhanced steady-state performance obtained using policies previously developed in the literature.
