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Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors.
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition
For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information.
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