51,860 research outputs found
Secure and Privacy-Preserving Average Consensus
Average consensus is fundamental for distributed systems since it underpins
key functionalities of such systems ranging from distributed information
fusion, decision-making, to decentralized control. In order to reach an
agreement, existing average consensus algorithms require each agent to exchange
explicit state information with its neighbors. This leads to the disclosure of
private state information, which is undesirable in cases where privacy is of
concern. In this paper, we propose a novel approach that enables secure and
privacy-preserving average consensus in a decentralized architecture in the
absence of any trusted third-parties. By leveraging homomorphic cryptography,
our approach can guarantee consensus to the exact value in a deterministic
manner. The proposed approach is light-weight in computation and communication,
and applicable to time-varying interaction topology cases. A hardware
implementation is presented to demonstrate the capability of our approach.Comment: 7 pages, 4 figures, paper is accepted to CPS-SPC'1
Time scale modeling for consensus in sparse directed networks with time-varying topologies
The paper considers the consensus problem in large networks represented by
time-varying directed graphs. A practical way of dealing with large-scale
networks is to reduce their dimension by collapsing the states of nodes
belonging to densely and intensively connected clusters into aggregate
variables. It will be shown that under suitable conditions, the states of the
agents in each cluster converge fast toward a local agreement. Local agreements
correspond to aggregate variables which slowly converge to consensus. Existing
results concerning the time-scale separation in large networks focus on fixed
and undirected graphs. The aim of this work is to extend these results to the
more general case of time-varying directed topologies. It is noteworthy that in
the fixed and undirected graph case the average of the states in each cluster
is time-invariant when neglecting the interactions between clusters. Therefore,
they are good candidates for the aggregate variables. This is no longer
possible here. Instead, we find suitable time-varying weights to compute the
aggregate variables as time-invariant weighted averages of the states in each
cluster. This allows to deal with the more challenging time-varying directed
graph case. We end up with a singularly perturbed system which is analyzed by
using the tools of two time-scales averaging which seem appropriate to this
system
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