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In this paper, we study the problem of privacy preservation of the continuous-time Laplacian static average consensus algorithm using additive perturbation signals. We consider this problem over a strongly connected and weight-balanced digraph. Starting from a local reference value, in static average consensus algorithm each agent constantly communicates with its neighboring agents to update its local state to compute the average of the reference values across the network. Since every agent transmits its local reference value to its in-neighbors, the reference value of the agents are trivially disclosed. In this paper, we investigate the possibility of preserving the privacy of the reference value of the agents by adding admissible perturbation signals to the local dynamics and the transmitted out signals of the agents. Admissible additive perturbation signals are those signals that do not perturb the final convergence point of the algorithm from the average of the reference values of the agents. Our results show that if an adversarial agent has access to the output of another agent and all the input signals transmitted to that agent, the adversary can discover the private reference value of that agent, regardless of the perturbation signals. Otherwise, the privacy of the agent can be preserved. We demonstrate our results through a numerical example.
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Extremum Seeking and Adaptive Dynamic Programming for Distributed Feedback Optimization
This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach that uses upper-level coordinators to adjust reference signals toward the global optimum and lower-level controllers to regulate agents’ outputs toward the reference signals. In the absence of precise information on local gradients and agent dynamics, an extremum-seeking mechanism is used to enforce a gradient descent optimization strategy, and an adaptive dynamic programming approach is taken to synthesize an internal-model-based optimal tracking controller. The whole procedure relies only on measurements of local costs and input-state data along agents’ trajectories. Moreover, under appropriate conditions, the closed-loop signals are bounded and the output of the agents exponentially converges to a small neighborhood of the desired extremum. A numerical example is conducted to validate the efficacy of the proposed method.
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- PAR ID:
- 10601374
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-1633-9
- Page Range / eLocation ID:
- 7662 to 7667
- Subject(s) / Keyword(s):
- Extremum Seeking Adaptive Dynamic Programming Distributed Feedback Optimization
- Format(s):
- Medium: X
- Location:
- Milan, Italy
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC56724.2024.10886832
