FedSysID: A Federated Approach to Sample-Efficient System Identification

• URL: http://arxiv.org/abs/2211.14393v1
• Date: Fri, 25 Nov 2022 22:24:49 GMT
• Title: FedSysID: A Federated Approach to Sample-Efficient System Identification
• Authors: Han Wang, Leonardo F. Toso, James Anderson
• Abstract summary: We study the problem of learning a linear system model from the observations of $M$ clients. We pose this problem as a federated learning problem and characterize the tension between achievable performance and system heterogeneity.
• Score: 3.7677951749356686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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