Scalar Federated Learning for Linear Quadratic Regulator
We propose ScalarFedLQR, a communication-efficient federated algorithm for model-free learning of a common policy in linear quadratic regulator (LQR) control of cooperative agents. The method builds on a decomposed projected gradient mechanism, in which each agent communicates only a scalar projection of a local zeroth-order gradient estimate. The server aggregates these scalar messages to reconstruct a global descent direction, reducing per-agent uplink communication from O(d) to O(1), independent of the policy dimension. Crucially, the projection-induced approximation error diminishes as the number of participating agents increases, yielding a favorable scaling law: larger fleets enable...
| Authors | Mohammadreza Rostami, Shahriar Talebi, Solmaz S. Kia |
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| Primary category | eess.SY |
| Published | 6 April 2026 |
| Last updated at source | 1 October 2026 |
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