Distributed Learning in Wireless Sensor Networks
arXiv:cs/0503072 · doi:10.1109/MSP.2006.1657817
Abstract
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.
Published in the Proceedings of the 42nd Annual Allerton Conference on Communication, Control and Computing, University of Illinois, 2004
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Cited by in corpus (50)
- Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
- Multitask Diffusion Adaptation over Networks
- Diffusion LMS over Multitask Networks
- Chebyshev Polynomial Approximation for Distributed Signal Processing
- Edge Intelligence: Architectures, Challenges, and Applications
- ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning
- Distributed Variational Bayesian Algorithms Over Sensor Networks
- On the Convergence of Decentralized Gradient Descent
- On reducing the communication cost of the diffusion LMS algorithm
- On the Influence of Informed Agents on Learning and Adaptation over Networks
- Understanding Game Theory via Wireless Power Control
- Consistency in Models for Distributed Learning under Communication Constraints
- Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
- Distributed Estimation, Information Loss and Exponential Families
- Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals
- A Framework for Parallel and Distributed Training of Neural Networks
- Decentralized Clustering and Linking by Networked Agents
- Distributed optimization in wireless sensor networks: an island-model framework
- Distributed Adaptive Learning Under Communication Constraints
- Incremental Stochastic Subgradient Algorithms for Convex Optimization
- Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach
- Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
- Dimensionally Distributed Learning: Models and Algorithm
- Designing Asymmetric Shift Operators for Decentralized Subspace Projection
- Function Computation Under Privacy, Secrecy, Distortion, and Communication Constraints
- Nested Distributed Gradient Methods with Adaptive Quantized Communication
- A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
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- Balancing Communication and Computation in Distributed Optimization
- Differentially Private ADMM for Convex Distributed Learning: Improved Accuracy via Multi-Step Approximation
- Protocols for Learning Classifiers on Distributed Data
- Cooperative Training for Attribute-Distributed Data: Trade-off Between Data Transmission and Performance
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- Balancing Lifetime and Classification Accuracy of Wireless Sensor Networks