Distributed Networked Learning with Correlated Data

TL;DR

This paper introduces a distributed estimation method for correlated data across networked nodes, balancing convergence speed and model precision, with applications in sensor networks and behavioral modeling.

Contribution

It presents a novel network regularization approach for distributed linear model estimation, analyzing its convergence and comparing it to federated learning.

Findings

Finite-time convergence characterization of the ensemble estimate.

Trade-off analysis showing faster updates in federated learning versus higher precision in networked estimation.

Successful application to sensor network data and bird behavior modeling.

Abstract

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.