Scalar Federated Learning for Linear Quadratic Regulator
SCALARFEDLQR is a communication-efficient method for federated learning to solve Linear Quadratic Regulator (LQR) control problems across many agents. It drastically reduces per-agent communication from O(d) to O(1) by having agents send only a single scalar projection of their local gradient estimate. This allows the server to reconstruct a global descent direction, enabling fast linear convergence while maintaining stability.