Cloud-Enabled Differentially Private Multiagent Optimization With Constraints

We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents' state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraint...

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Vydané v:	IEEE transactions on control of network systems Ročník 5; číslo 4; s. 1693 - 1706
Hlavní autori:	Hale, Matthew T., Egerstedt, Magnus
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Cloud computing Computer simulation Convergence Data privacy Decentralized control Eavesdropping Linear programming Multiagent systems Networked control systems Optimization Privacy Trajectories Trajectory
ISSN:	2325-5870, 2372-2533
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