Multitask Diffusion Adaptation Over Asynchronous Networks

• Published in: IEEE transactions on signal processing Vol. 64; no. 11; pp. 2835 - 2850
• Main Authors: Nassif, Roula, Richard, Cedric, Ferrari, Andre, Sayed, Ali H.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: 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
• DOI: 10.1109/TSP.2016.2518991

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.