87 research outputs found
Distributed Learning in Wireless Sensor Networks
The problem of distributed or decentralized detection and estimation in
applications such as wireless sensor networks has often been considered in the
framework of parametric models, in which strong assumptions are made about a
statistical description of nature. In certain applications, such assumptions
are warranted and systems designed from these models show promise. However, in
other scenarios, prior knowledge is at best vague and translating such
knowledge into a statistical model is undesirable. Applications such as these
pave the way for a nonparametric study of distributed detection and estimation.
In this paper, we review recent work of the authors in which some elementary
models for distributed learning are considered. These models are in the spirit
of classical work in nonparametric statistics and are applicable to wireless
sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference
on Communication, Control and Computing, University of Illinois, 200
Learning from distributed data sources using random vector functional-link networks
One of the main characteristics in many real-world big data scenarios is their distributed nature. In a machine learning context, distributed data, together with the requirements of preserving privacy and scaling up to large networks, brings the challenge of designing fully decentralized training protocols. In this paper, we explore the problem of distributed learning when the features of every pattern are available throughout multiple agents (as is happening, for example, in a distributed database scenario). We propose an algorithm for a particular class of neural networks, known as Random Vector Functional-Link (RVFL), which is based on the Alternating Direction Method of Multipliers optimization algorithm. The proposed algorithm allows to learn an RVFL network from multiple distributed data sources, while restricting communication to the unique operation of computing a distributed average. Our experimental simulations show that the algorithm is able to achieve a generalization accuracy comparable to a fully centralized solution, while at the same time being extremely efficient
Multitask Diffusion Adaptation over Networks
Adaptive networks are suitable for decentralized inference tasks, e.g., to
monitor complex natural phenomena. Recent research works have intensively
studied distributed optimization problems in the case where the nodes have to
estimate a single optimum parameter vector collaboratively. However, there are
many important applications that are multitask-oriented in the sense that there
are multiple optimum parameter vectors to be inferred simultaneously, in a
collaborative manner, over the area covered by the network. In this paper, we
employ diffusion strategies to develop distributed algorithms that address
multitask problems by minimizing an appropriate mean-square error criterion
with -regularization. The stability and convergence of the algorithm in
the mean and in the mean-square sense is analyzed. Simulations are conducted to
verify the theoretical findings, and to illustrate how the distributed strategy
can be used in several useful applications related to spectral sensing, target
localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio
Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
We propose an adaptive scheme for distributed learning of nonlinear functions
by a network of nodes. The proposed algorithm consists of a local adaptation
stage utilizing multiple kernels with projections onto hyperslabs and a
diffusion stage to achieve consensus on the estimates over the whole network.
Multiple kernels are incorporated to enhance the approximation of functions
with several high and low frequency components common in practical scenarios.
We provide a thorough convergence analysis of the proposed scheme based on the
metric of the Cartesian product of multiple reproducing kernel Hilbert spaces.
To this end, we introduce a modified consensus matrix considering this specific
metric and prove its equivalence to the ordinary consensus matrix. Besides, the
use of hyperslabs enables a significant reduction of the computational demand
with only a minor loss in the performance. Numerical evaluations with synthetic
and real data are conducted showing the efficacy of the proposed algorithm
compared to the state of the art schemes.Comment: Double-column 15 pages, 10 figures, submitted to IEEE Trans. Signal
Processin
Understanding Game Theory via Wireless Power Control
In this lecture note, we introduce the basic concepts of game theory (GT), a
branch of mathematics traditionally studied and applied in the areas of
economics, political science, and biology, which has emerged in the last
fifteen years as an effective framework for communications, networking, and
signal processing (SP). The real catalyzer has been the blooming of all issues
related to distributed networks, in which the nodes can be modeled as players
in a game competing for system resources. Some relevant notions of GT are
introduced by elaborating on a simple application in the context of wireless
communications, notably the power control in an interference channel (IC) with
two transmitters and two receivers.Comment: Accepted for publication as lecture note in IEEE Signal Processing
Magazine, 13 pages, 4 figures. The results can be reproduced using the
following Matlab code: https://github.com/lucasanguinetti/ ln-game-theor
Nested Distributed Gradient Methods with Adaptive Quantized Communication
In this paper, we consider minimizing a sum of local convex objective
functions in a distributed setting, where communication can be costly. We
propose and analyze a class of nested distributed gradient methods with
adaptive quantized communication (NEAR-DGD+Q). We show the effect of performing
multiple quantized communication steps on the rate of convergence and on the
size of the neighborhood of convergence, and prove R-Linear convergence to the
exact solution with increasing number of consensus steps and adaptive
quantization. We test the performance of the method, as well as some practical
variants, on quadratic functions, and show the effects of multiple quantized
communication steps in terms of iterations/gradient evaluations, communication
and cost.Comment: 9 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1709.0299
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