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New federated learning algorithm reduces communication for LQR control

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ScalarFedLQR, a novel federated learning algorithm designed for linear quadratic regulator (LQR) control. This method significantly reduces communication overhead by having each agent transmit only a scalar projection of its gradient estimate, rather than the full gradient. The algorithm's efficiency improves with a larger number of agents, allowing for more accurate gradient recovery and faster convergence, even in high-dimensional scenarios. AI

IMPACT This research could enable more efficient distributed control systems in robotics and autonomous agents by reducing communication bottlenecks.

RANK_REASON Academic paper detailing a new algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New federated learning algorithm reduces communication for LQR control

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Academic paper detailing a new algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammadreza Rostami, Shahriar Talebi, Solmaz S. Kia ·

    Scalar Federated Learning for Linear Quadratic Regulator

    arXiv:2604.05088v2 Announce Type: replace-cross Abstract: 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 project…

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