Title:
Differentially Private Cloud-Based Multi-Agent Optimization with Constraints
Differentially Private Cloud-Based Multi-Agent Optimization with Constraints
Authors
Hale, M. T.
Egerstedt, Magnus B.
Egerstedt, Magnus B.
Advisors
Collections
Supplementary to
Permanent Link
Abstract
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.
Sponsor
Date Issued
2015-07
Extent
Resource Type
Text
Resource Subtype
Proceedings