A local-training algorithm for nonconvex distributed optimization achieves communication efficiency and differential privacy via gradient clipping plus additive noise, with proven convergence to a stationary point within bounded distance and formal privacy guarantees.
Separation of learning and control for cyber- physical systems
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An online algorithm for zero-sum LQ games with unknown dynamics combines model estimation and surrogate selection to achieve regret bounds on policy convergence.
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Communication-Efficient Distributed Learning with Differential Privacy
A local-training algorithm for nonconvex distributed optimization achieves communication efficiency and differential privacy via gradient clipping plus additive noise, with proven convergence to a stationary point within bounded distance and formal privacy guarantees.
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An Online Learning Approach for Two-Player Zero-Sum Linear Quadratic Games
An online algorithm for zero-sum LQ games with unknown dynamics combines model estimation and surrogate selection to achieve regret bounds on policy convergence.