Cloud-based optimization: A quasi-decentralized approach to multi-agent coordination

Hale, Matthew; Egerstedt, Magnus

doi:10.1109/cdc.2014.7040430

Cited by 15 publications

(12 citation statements)

References 26 publications

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“…The technology of cloud computing provides the ability to communicate with many agents, rapidly perform demanding computations, and broadcast the results. Moreover, these capabilities can be seamlessly and remotely added into networks of agents, making the cloud a natural choice of central aggregator for this work [15].…”

Section: B Cloud-based Aggregationmentioning

confidence: 99%

“…Adding noise makes this problem equivalent to a multi-agent linear quadratic Gaussian (LQG) problem, and the optimal controller will be linear in the expected value of agents' states. Computing this expected value is a centralized operation, and we therefore augment the network with a cloud computer [15]. In contrast to some existing approaches, the cloud is not a trusted third party and does not receive sensitive information from any agent [16].…”

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confidence: 99%

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Differentially Private LQ Control

Yazdani¹,

Jones²,

Leahy³

et al. 2018

Preprint

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As multi-agent systems proliferate and share more and more user data, new approaches are needed to protect sensitive data while still guaranteeing successful operation. To address this need, we present a private multi-agent LQ control framework. We consider problems in which each agent has linear dynamics and the agents are coupled by a quadratic cost. Generating optimal control values for the agents is a centralized operation, and we therefore introduce a cloud computer into the network for this purpose. The cloud is tasked with aggregating agents' outputs, computing control inputs, and transmitting these inputs to the agents, which apply them in their state updates. Agents' state information can be sensitive and we therefore protect it using differential privacy. Differential privacy is a statistical notion of privacy enforced by adding noise to sensitive data before sharing it, and agents will therefore add noise to all data before sending it to the cloud. The result is a private multi-agent LQG framework in which agents' states are protected from both the cloud and other agents. Adding noise to agents' data certainly impacts the performance of the system, and we provide a trade-off between agents' privacy levels and the entropy seen at the cloud as a measure of how difficult it is to compute control values when privacy is enforced. We further characterize this system by bounding the accuracy with which an adversary could predict agents' states by using the differentially private information they share. Simulation results are provided to validate the theoretical developments made. I. INTRODUCTION MANY multi-agent systems, such as smart power grids, robotic swarms, and traffic monitoring systems, require agents to exchange information to work together. In some cases, the information shared may be rather sensitive. For example, power consumption data in a smart power grid can expose certain habits, behaviors, and activities of an individual including his or her daily schedule [1]- [5]. Sensitive user data should therefore be protected when it is shared, though of course it must remain useful in multi-agent coordination. Hence, providing privacy in the setting of multi-agent control should protect sensitive data from the recipient of a transmission while still ensuring that transmitted data remains useful to that recipient.Recently, privacy of this form has been achieved using differential privacy. Differential privacy originates in the database literature, and was originally designed to protect sensitive data of individuals when databases are queried

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Section: B Cloud-based Aggregationmentioning

confidence: 99%

mentioning

confidence: 99%

Differentially Private LQ Control

Yazdani¹,

Jones²,

Leahy³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In [10], differentially private linear programs are solved while constraints or the objective function are kept private. In the current paper a saddle point finding algorithm in the vein of [8] is used to keep states private.…”

Section: Introductionmentioning

confidence: 99%

Differentially private cloud-based multi-agent optimization with constraints

Hale

Egerstedty

2015

2015 American Control Conference (ACC)

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We present an optimization framework that solves constrained multi-agent optimization problems while keeping each agent's state differentially private. The agents in the network seek to optimize a local objective function in the presence of global constraints. Agents communicate only through a trusted cloud computer and the cloud also performs computations based on global information. The cloud computer modifies the results of such computations before they are sent to the agents in order to guarantee that the agents' states are kept private. We show that under mild conditions each agent's optimization problem converges in mean-square to its unique solution while each agent's state is kept differentially private. A numerical simulation is provided to demonstrate the viability of this approach.

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“…Towards answering this question, we present here a multiagent optimization architecture in which a cloud computer is used to occasionally provide centralized information to a network of agents solving a nonlinear programming problem. This cloud-based optimization architecture was introduced in [5], though here we substantially broaden the class of problems to be solved and allow for communications delays when communicating with the cloud. The cloud carries out computations based on information sent to it by agents in the network, and intermittently disseminates these results to the agents for use in their own computations.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, each agent shares its state with some number of neighboring agents at each time. In [5], the assumption was made that all information in the network was synchronized at each time, so that all computations were relying on the same information. Here we eliminate this assumption and, as a consequence, delays occur which give rise to various kinds of errors.…”

Section: Introductionmentioning

confidence: 99%

Cloud-based centralized/decentralized multi-agent optimization with communication delays

Hale

Nedić

Egerstedt

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We present and analyze a computational hybrid architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constraint functions with mild smoothness conditions imposed on them. For such problems, we provide a primaldual algorithm implemented in the hybrid architecture, which consists of a decentralized network of agents into which centralized information is occasionally injected, and we establish its convergence properties. To accomplish this, a central cloud computer aggregates global information, carries out computations of the dual variables based on this information, and then distributes the updated dual variables to the agents. The agents update their (primal) state variables and also communicate among themselves with each agent sharing and receiving state information with some number of its neighbors. Throughout, communications with the cloud are not assumed to be synchronous or instantaneous, and communication delays are explicitly accounted for in the modeling and analysis of the system. Experimental results are presented to support the theoretical developments made.

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Cloud-based optimization: A quasi-decentralized approach to multi-agent coordination

Cited by 15 publications

References 26 publications

Differentially Private LQ Control

Differentially Private LQ Control

Differentially private cloud-based multi-agent optimization with constraints

Cloud-based centralized/decentralized multi-agent optimization with communication delays

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