915 research outputs found
Consensus of Multi-Agent Networks in the Presence of Adversaries Using Only Local Information
This paper addresses the problem of resilient consensus in the presence of
misbehaving nodes. Although it is typical to assume knowledge of at least some
nonlocal information when studying secure and fault-tolerant consensus
algorithms, this assumption is not suitable for large-scale dynamic networks.
To remedy this, we emphasize the use of local strategies to deal with
resilience to security breaches. We study a consensus protocol that uses only
local information and we consider worst-case security breaches, where the
compromised nodes have full knowledge of the network and the intentions of the
other nodes. We provide necessary and sufficient conditions for the normal
nodes to reach consensus despite the influence of the malicious nodes under
different threat assumptions. These conditions are stated in terms of a novel
graph-theoretic property referred to as network robustness.Comment: This report contains the proofs of the results presented at HiCoNS
201
Static consensus in passifiable linear networks
Sufficient conditions of consensus (synchronization) in networks described by
digraphs and consisting of identical determenistic SIMO systems are derived.
Identical and nonidentical control gains (positive arc weights) are considered.
Connection between admissible digraphs and nonsmooth hypersurfaces (sufficient
gain boundary) is established. Necessary and sufficient conditions for static
consensus by output feedback in networks consisting of certain class of double
integrators are rediscovered. Scalability for circle digraph in terms of gain
magnitudes is studied. Examples and results of numerical simulations are
presented.Comment: 13 pages, 5 figure
Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs
We study a general framework for broadcast gossip algorithms which use
companion variables to solve the average consensus problem. Each node maintains
an initial state and a companion variable. Iterative updates are performed
asynchronously whereby one random node broadcasts its current state and
companion variable and all other nodes receiving the broadcast update their
state and companion variable. We provide conditions under which this scheme is
guaranteed to converge to a consensus solution, where all nodes have the same
limiting values, on any strongly connected directed graph. Under stronger
conditions, which are reasonable when the underlying communication graph is
undirected, we guarantee that the consensus value is equal to the average, both
in expectation and in the mean-squared sense. Our analysis uses tools from
non-negative matrix theory and perturbation theory. The perturbation results
rely on a parameter being sufficiently small. We characterize the allowable
upper bound as well as the optimal setting for the perturbation parameter as a
function of the network topology, and this allows us to characterize the
worst-case rate of convergence. Simulations illustrate that, in comparison to
existing broadcast gossip algorithms, the approaches proposed in this paper
have the advantage that they simultaneously can be guaranteed to converge to
the average consensus and they converge in a small number of broadcasts.Comment: 30 pages, submitte
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication
This paper proposes a novel class of distributed continuous-time coordination
algorithms to solve network optimization problems whose cost function is a sum
of local cost functions associated to the individual agents. We establish the
exponential convergence of the proposed algorithm under (i) strongly connected
and weight-balanced digraph topologies when the local costs are strongly convex
with globally Lipschitz gradients, and (ii) connected graph topologies when the
local costs are strongly convex with locally Lipschitz gradients. When the
local cost functions are convex and the global cost function is strictly
convex, we establish asymptotic convergence under connected graph topologies.
We also characterize the algorithm's correctness under time-varying interaction
topologies and study its privacy preservation properties. Motivated by
practical considerations, we analyze the algorithm implementation with
discrete-time communication. We provide an upper bound on the stepsize that
guarantees exponential convergence over connected graphs for implementations
with periodic communication. Building on this result, we design a
provably-correct centralized event-triggered communication scheme that is free
of Zeno behavior. Finally, we develop a distributed, asynchronous
event-triggered communication scheme that is also free of Zeno with asymptotic
convergence guarantees. Several simulations illustrate our results.Comment: 12 page
Distributed Big-Data Optimization via Block-Iterative Convexification and Averaging
In this paper, we study distributed big-data nonconvex optimization in
multi-agent networks. We consider the (constrained) minimization of the sum of
a smooth (possibly) nonconvex function, i.e., the agents' sum-utility, plus a
convex (possibly) nonsmooth regularizer. Our interest is in big-data problems
wherein there is a large number of variables to optimize. If treated by means
of standard distributed optimization algorithms, these large-scale problems may
be intractable, due to the prohibitive local computation and communication
burden at each node. We propose a novel distributed solution method whereby at
each iteration agents optimize and then communicate (in an uncoordinated
fashion) only a subset of their decision variables. To deal with non-convexity
of the cost function, the novel scheme hinges on Successive Convex
Approximation (SCA) techniques coupled with i) a tracking mechanism
instrumental to locally estimate gradient averages; and ii) a novel block-wise
consensus-based protocol to perform local block-averaging operations and
gradient tacking. Asymptotic convergence to stationary solutions of the
nonconvex problem is established. Finally, numerical results show the
effectiveness of the proposed algorithm and highlight how the block dimension
impacts on the communication overhead and practical convergence speed
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