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arXiv · 2211.14393

FedSysID: A Federated Approach to Sample-Efficient System Identification

Han Wang·Leonardo F. Toso·James Anderson
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Abstract

We study the problem of learning a linear system model from the observations of $M$ clients. The catch: Each client is observing data from a different dynamical system. This work addresses the question of how multiple clients collaboratively learn dynamical models in the presence of heterogeneity. We pose this problem as a federated learning problem and characterize the tension between achievable performance and system heterogeneity. Furthermore, our federated sample complexity result provides a constant factor improvement over the single agent setting. Finally, we describe a meta federated learning algorithm, FedSysID, that leverages existing federated algorithms at the client level.

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BibTeXRIS

Han Wang, Leonardo F. Toso, James Anderson. 2022-11-25. FedSysID: A Federated Approach to Sample-Efficient System Identification. https://arxiv.org/abs/2211.14393

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