2,911 research outputs found
Coordination and Privacy Preservation in Multi-Agent Systems
This dissertation considers two key problems in multi-agent systems: coordination (including both synchronization and desynchronization) and privacy preservation.
For coordination in multi-agent systems, we focus on synchronization/desynchronization of distributed pulse-coupled oscillator (PCO) networks and their applications in collective motion coordination. Pulse-coupled oscillators were originally proposed to model synchronization in biological systems such as flashing fireflies and firing neurons. In recent years, with proven scalability, simplicity, accuracy, and robustness, the PCO based synchronization strategy has become a powerful clock synchronization primitive for wireless sensor networks. Driven by these increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators has gained increased popularity. However, most existing results address the local synchronization of PCOs with initial phases constrained in a half cycle, and results on global synchronization from any initial condition are very sparse. In our work, we address global PCO synchronization from an arbitrary phase distribution under chain or directed tree graphs. More importantly, different from existing global synchronization studies on decentralized PCO networks, our work allows heterogeneous coupling functions and perturbations on PCOs\u27 natural frequencies, and our results hold under any coupling strength between zero and one, which is crucial because a large coupling strength has been shown to be detrimental to the robustness of PCO synchronization to disturbances.
Compared with synchronization, desynchronization of PCOs is less explored. Desynchronization spreads the phase variables of all PCOs uniformly apart (with equal difference between neighboring phases). It has also been found in many biological phenomena, such as neuron spiking and fish signaling. Recently, phase desynchronization has been employed to achieve round-robin scheduling, which is crucial in applications as diverse as media access control of communication networks, realization of analog-to-digital converters, and scheduling of traffic flows in intersections. In our work, we systematically characterize pulse-coupled oscillators based decentralized phase desynchronization and propose an interaction function that is more general than existing results. Numerical simulations show that the proposed pulse based interaction function also has better robustness to pulse losses, time delays, and frequency errors than existing results.
Collective motion coordination is fundamental in systems as diverse as mobile sensor networks, swarm robotics, autonomous vehicles, and animal groups. Inspired by the close relationship between phase synchronization/desynchronization of PCOs and the heading dynamics of connected vehicles/robots, we propose a pulse-based integrated communication and control approach for collective motion coordination. Our approach only employs simple and identical pulses, which significantly reduces processing latency and communication delay compared with conventional packet based communications. Not only can heading control be achieved in the proposed approach to coordinate the headings (orientations) of motions in a network, but also spacing control for circular motion is achievable to design the spacing between neighboring nodes (e.g., vehicles or robots).
The second part of this dissertation is privacy preservation in multi-agent systems. More specifically, we focus on privacy-preserving average consensus as it is key for multi-agent systems, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual nodes (agents) to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In our work, we propose a novel average consensus algorithm for time-varying directed graphs which can protect the privacy of participating nodes\u27 initial states. Leveraging algorithm-level obfuscation, the algorithm does not need the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can guarantee privacy of participating nodes without compromising the accuracy of consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable privacy through compromising accuracy in obtained consensus value. The approach is able to protect the privacy of participating nodes even in the presence of multiple honest-but-curious nodes which can collude with each other
Tailoring Gradient Methods for Differentially-Private Distributed Optimization
Decentralized optimization is gaining increased traction due to its
widespread applications in large-scale machine learning and multi-agent
systems. The same mechanism that enables its success, i.e., information sharing
among participating agents, however, also leads to the disclosure of individual
agents' private information, which is unacceptable when sensitive data are
involved. As differential privacy is becoming a de facto standard for privacy
preservation, recently results have emerged integrating differential privacy
with distributed optimization. Although such differential-privacy based privacy
approaches for distributed optimization are efficient in both computation and
communication, directly incorporating differential privacy design in existing
distributed optimization approaches significantly compromises optimization
accuracy. In this paper, we propose to redesign and tailor gradient methods for
differentially-private distributed optimization, and propose two
differential-privacy oriented gradient methods that can ensure both privacy and
optimality. We prove that the proposed distributed algorithms can ensure almost
sure convergence to an optimal solution under any persistent and
variance-bounded differential-privacy noise, which, to the best of our
knowledge, has not been reported before. The first algorithm is based on
static-consensus based gradient methods and only shares one variable in each
iteration. The second algorithm is based on dynamic-consensus
(gradient-tracking) based distributed optimization methods and, hence, it is
applicable to general directed interaction graph topologies. Numerical
comparisons with existing counterparts confirm the effectiveness of the
proposed approaches
A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics
In multiagent dynamical systems, privacy protection corresponds to avoid
disclosing the initial states of the agents while accomplishing a distributed
task. The system-theoretic framework described in this paper for this scope,
denoted dynamical privacy, relies on introducing output maps which act as
masks, rendering the internal states of an agent indiscernible by the other
agents as well as by external agents monitoring all communications. Our output
masks are local (i.e., decided independently by each agent), time-varying
functions asymptotically converging to the true states. The resulting masked
system is also time-varying, and has the original unmasked system as its limit
system. When the unmasked system has a globally exponentially stable
equilibrium point, it is shown in the paper that the masked system has the same
point as a global attractor. It is also shown that existence of equilibrium
points in the masked system is not compatible with dynamical privacy.
Application of dynamical privacy to popular examples of multiagent dynamics,
such as models of social opinions, average consensus and synchronization, is
investigated in detail.Comment: 38 pages, 4 figures, extended version of arXiv preprint
arXiv:1808.0808
A dynamical approach to privacy preserving average consensus
In this paper we propose a novel method for achieving average consensus in a
continuous-time multiagent network while avoiding to disclose the initial
states of the individual agents. In order to achieve privacy protection of the
state variables, we introduce maps, called output masks, which alter the value
of the states before transmitting them. These output masks are local (i.e.,
implemented independently by each agent), deterministic, time-varying and
converging asymptotically to the true state. The resulting masked system is
also time-varying and has the original (unmasked) system as its limit system.
It is shown in the paper that the masked system has the original average
consensus value as a global attractor. However, in order to preserve privacy,
it cannot share an equilibrium point with the unmasked system, meaning that in
the masked system the global attractor cannot be also stable.Comment: 19 pages, 2 figures (minor changes w.r.t. previous version
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