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首页> 外文期刊>Information Theory, IEEE Transactions on >Information Exchange and Learning Dynamics Over Weakly Connected Adaptive Networks
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Information Exchange and Learning Dynamics Over Weakly Connected Adaptive Networks

机译：弱连接自适应网络上的信息交换和学习动力学

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摘要

This paper examines the learning mechanism of adaptive agents over weakly connected graphs and reveals an interesting behavior on how information flows through such topologies. The results clarify how asymmetries in the exchange of data can mask local information at certain agents and make them totally dependent on other agents. A leader-follower relationship develops with the performance of some agents being fully determined by the performance of other agents that are outside their domain of influence. This scenario can arise, for example, due to intruder attacks by malicious agents or as the result of failures by some critical links. The findings in this paper help explain why strong-connectivity of the network topology, adaptation of the combination weights, and clustering of agents are important ingredients to equalize the learning abilities of all agents against such disturbances. The results also clarify how weak-connectivity can be helpful in reducing the effect of outlier data on learning performance.
机译：本文研究了弱连接图中自适应代理的学习机制，并揭示了有关信息如何通过此类拓扑流动的有趣行为。结果表明，数据交换中的不对称性如何掩盖某些代理商的本地信息，并使它们完全依赖于其他代理商。领导者与跟随者关系的发展是由某些特工的绩效完全取决于其影响范围之外的其他特工的绩效来决定的。例如，由于恶意代理的入侵者攻击或某些关键链接的失败，可能导致出现这种情况。本文中的发现有助于解释为什么网络拓扑的强连通性，组合权重的自适应以及代理的聚集是使所有代理针对此类干扰的学习能力均等的重要因素。结果还阐明了弱连接性如何有助于减少异常数据对学习成绩的影响。

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