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Distributed networked learning with correlated data
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DOI：10.1016/j.automatica.2021.110134.png)
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摘要
En 中文
We consider a distributed estimation method in a setting with heterogeneous streams of correlated data distributed across nodes in a network. In the considered approach, linear models are estimated locally (i.e., with only local data) subject to a network regularization term that penalizes a local model that differs from neighboring models. We analyze computation dynamics (associated with stochastic gradient updates) and information exchange (associated with exchanging current models with neighboring nodes). We provide a finite-time characterization of convergence of the weighted ensemble average estimate and compare this result to federated learning, an alternative approach to estimation wherein a single model is updated by locally generated gradient updates. This comparison highlights the trade-off between speed vs precision: while model updates take place at a faster rate in federated learning, the proposed networked approach to estimation enables the identification of models with higher precision. We illustrate the method's general applicability in two examples: estimating a Markov random field using wireless sensor networks and modeling prey escape behavior of flocking birds based on a publicly available dataset. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Large scale optimization problems and methods
Network-based computing systems
Learning theory
Statistical analysis
Parameter and state estimation
Multi-agent systems
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期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
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Distributed weighted least-squares estimation with fast convergence for large-scale systems.png)
AUTOMATICAOAAI
AUTOMATICAIF5.9
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