Multitask Diffusion Adaptation Over Asynchronous Networks

The multitask diffusion LMS is an efficient strategy to simultaneously infer, in a collaborative manner, multiple parameter vectors. Existing works on multitask problems assume that all agents respond to data synchronously. In several applications, agents may not be able to act synchronously because...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 64; H. 11; S. 2835 - 2850
Hauptverfasser: Nassif, Roula, Richard, Cedric, Ferrari, Andre, Sayed, Ali H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Astrophysics
asynchronous networks
Context
Correlation
diffusion adaptation
Distributed optimization
Engineering Sciences
Estimation
Instrumentation and Methods for Astrophysic
Machine Learning
Mean square error methods
mean-square performance analysis
multitask learning
Network topology
Optimization
Sciences of the Universe
Signal and Image processing
Statistics
Topology
ISSN:1053-587X, 1941-0476
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Zusammenfassung:The multitask diffusion LMS is an efficient strategy to simultaneously infer, in a collaborative manner, multiple parameter vectors. Existing works on multitask problems assume that all agents respond to data synchronously. In several applications, agents may not be able to act synchronously because networks can be subject to several sources of uncertainties such as changing topology, random link failures, or agents turning on and off for energy conservation. In this paper, we describe a model for the solution of multitask problems over asynchronous networks and carry out a detailed mean and mean-square error analysis. Results show that sufficiently small step-sizes can still ensure both stability and performance. Simulations and illustrative examples are provided to verify the theoretical findings.
Bibliographie:ObjectType-Article-1
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2016.2518991