2014 AdaptationLearningandOptimizati

• (Sayed, 2014) ⇒ Ali Sayed. (2014). “Adaptation, Learning, and Optimization over Networks.” In: Foundations and Trends® in Machine Learning Journal, 7(4-5). doi:10.1561/2200000051

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Adaptation%2C+Learning%2C+and+Optimization+over+Networks
• http://dl.acm.org/citation.cfm?id=2693107.2693108&preflayout=flat#citedby

Quotes

Author Keywords

• Multi-agent learning; Multi-agent optimization; Multi-agent adaptation; Distributed optimization; Adaptive networks; Diffusion strategies; Consensus strategies; Incremental strategies; Stochastic-gradient algorithms; Distributed stochastic-gradient algori

Abstract

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and [[network design|design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. The results derived in this work are useful in comparing network topologies against each other, and in comparing networked solutions against centralized or batch implementations. There are many good reasons for the peaked interest in distributed implementations, especially in this day and age when the word “network” has become commonplace whether one is referring to social networks, power networks, transportation networks, biological networks, or other types of networks. Some of these reasons have to do with the benefits of cooperation in terms of improved performance and improved resilience to failure. Other reasons deal with privacy and secrecy considerations where agents may not be comfortable sharing their data with remote fusion centers. In other situations, the data may already be available in dispersed locations, as happens with cloud computing. One may also be interested in learning through data mining from big data sets. Motivated by these considerations, this work examines the limits of performance of distributed solutions and discusses procedures that help bring forth their potential more fully. The presentation adopts a useful statistical framework and derives performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks. At the same time, the work illustrates how distributed processing over graphs gives rise to some revealing phenomena due to the coupling effect among the agents. These phenomena are discussed in the context of adaptive networks, along with examples from a variety of areas including distributed sensing, intrusion detection, distributed estimation, online adaptation, network system theory, and machine learning.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 AdaptationLearningandOptimizati	Ali Sayed			Adaptation, Learning, and Optimization over Networks				10.1561/2200000051		2014