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Keywords:
differential privacy; safe consensus; cooperative-competitive multi-agent systems; Laplace distribution; $(p, r)$-accuracy
differential privacy; safe consensus; cooperative-competitive multi-agent systems; Laplace distribution; $(p, r)$-accuracy
Summary:
This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and $(p,r)$-accuracy. Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors. Thus, a practical method is established for the agents to select their own privacy levels. Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.
References:
[1] Altafini, C.: Consensus problems on networks with antagonistic interactions. IEEE Trans. Automat. Control 58 (2013), 935-946. DOI | MR 3038795
[2] Cihan, O.: Topology design for group consensus in directed multi-agent systems. Kybernetika 56 (2020),578-597. DOI | MR 4131744
[3] Chen, B., Hu, J., Zhao, Y., Ghosh, B. K.: Finite-time velocity-free rendezvous control of multiple AUV systems with intermittent communication. IEEE Trans. Syst. Man Cybernet. Syst. XX (2022), 1-12. DOI
[4] Chen, Z., Qin, J., .Li, B, Qi, H., Buchhorn, P., Shi, G.: Dynamics of opinions with social biases. Automatica 106 (2019), 374-383. DOI | MR 3954046
[5] Du, Y., Wang, Y., Zuo, Z.: Mean square bipartite consensus for multiagent systems with antagonistic information and time-varying topologies. IEEE Trans. Syst. Man Cybernet. Syst. 52 (2020), 1744-1754. DOI
[6] Dwork, C.: Differential privacy: A survey of results. In: Proc. 5th International Conference on Theory and Applications of Models of Computation (2008), pp. 1-19. MR 2472670
[7] Gao, L., Deng, S., Ren, W., Hu, C.: Differentially private consensus with quantized communication. IEEE Trans. Cybernet. 51 (2021), 4075-4088. DOI
[8] He, J., Cai, L., Guan, X.: Differential private noise adding mechanism and its application on consensus algorithm. IEEE Trans. Signal Process. 68 (2020), 4069-4082. DOI | MR 4128133
[9] Hu, J.: On robust consensus of multi-agent systems with communication delays. Kybernetika 45 (2009), 768-784. DOI | MR 2599111 | Zbl 1190.93003
[10] Hu, J., Wu, Y.: Interventional bipartite consensus on coopetition networks with unknown dynamics. J. Franklin. Inst. 354 (2017), 4438-4456. DOI | MR 3655777
[11] Hu, J., Wu, Y., Li, T., Ghosh, B. K.: Consensus control of general linear multiagent systems with antagonistic interactions and communication noises. IEEE Trans. Automat. Control 64 (2019), 2122-2127. DOI | MR 3951056
[12] Huang, Z., Mitra, S., Dullerud, G.: Differentially private iterative synchronous consensus. In: Proc. 2012 ACM Workshop on Privacy in the Electronic Society (2012) pp. 81-89.
[13] Li, H., Li, X.: Distributed consensus of heterogeneous linear time-varying systems on UAVs-USVs coordination. IEEE Trans. Circuits Syst. II Express Briefs 67 (2020), 1264-1268. DOI
[14] Li, P., Hu, J., Qiu, L., Zhao, Y., Ghosh, B. K.: Distributed economic dispatch strategy for power-water networks. IEEE Trans. Control Netw. Syst. 9 (2022), 356-366. DOI | MR 4450544
[15] Liu, X., Zhang, J., Wang, J.: Differentially private consensus algorithm for continuous-time heterogeneous multi-agent systems. Automatica 122 (2020), 109283. DOI | MR 4161365
[16] Ma, C., Xie, L.: Necessary and sufficient conditions for leader-following bipartite consensus with measurement noise. IEEE Trans. Syst. Man Cybernet. Syst. 50 (2020), 1976-1981. DOI
[17] Nozari, E., Tallapragada, P., Cortes, J.: Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design. Automatica 81 (2017), 221-231. DOI | MR 3654605
[18] Peng, Z., Zhao, Y., Hu, J., Luo, R., Ghosh, B. K., Nguang, S. K.: Input-output data-based output antisynchronization control of multi-agent systems using reinforcement learning approach. IEEE Trans. Industr. Inform. 17 (2021), 7359-7367. DOI
[19] Rehák, B., Lynnyk, V.: Consensus of a multi-agent systems with heterogeneous delays. Kybernetika 56 (2020), 363-381. DOI | MR 4103722
[20] Tang, Y.: Output average consensus over heterogeneous multi-agent systems via two-level approach. Kybernetika 53 (2017), 282-295. DOI | MR 3661353
[21] Tang, Y.: Multi-agent optimal consensus with unknown control directions. IEEE Control Systems Lett. 5 (2021), 1201-1206. DOI | MR 4211660
[22] Tang, Y., Wang, X.: Optimal output consensus for nonlinear multiagent systems with both static and dynamic uncertainties. IEEE Trans. Automat. Control 66 (2021), 1733-1740. DOI | MR 4240200
[23] Wang, L., Liu, Y., Manchester, I., Shi, G.: Differentially private distributed computation via public-private communication networks. arXiv preprint arXiv:2101.01376, 2021 MR 4138618
[24] Wang, Y., Lam, J., Lin, H.: Differentially private average consensus with general directed graphs. Neurocomputing 458 (2021), 87-98. DOI
[25] Wang, X., He, J., Cheng, P., Chen, J.: Differentially private maximum consensus: Design, analysis and impossibility result. IEEE Trans. Netw. Sci. Engrg. 6 (2019), 928-939. DOI | MR 4051631
[26] Wu, Y., Zhao, Y., Hu, J.: Bipartite consensus control of high-order multiagent systems with unknown disturbances. IEEE Trans. Syst. Man Cybernet. Syst. 49 (2019), 2189-2199. DOI
[27] Zhang, Y., Hong, Y. Lou Y., Xie, L.: Distributed projection-based algorithms for source localization in wireless sensor networks. IEEE Trans. Wirel. Commun. 14 (2015), 3131-3142. DOI
[28] Zuo, Z., Tian, R., Han, Q., Wang, Y., Zhang, W.: Differential privacy for bipartite consensus over signed digraph. Neurocomputing 468 (2022), 11-21. DOI
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