TY - GEN
T1 - Enhance Privacy Protection by Reducing Information Exchanges in Multi-agent System Consensus
AU - Tian, Sheng
AU - Shen, Hong
AU - Ke, Wei
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Frequent (agent-state) information exchanges for consensus in a multi-agent system (MAS) increases not only communication overhead but also the risk of sensitive information disclosure. This paper addresses the challenge of how to enhance data privacy protection by reducing information exchanges in MAS consensus based on event-trigger control (ETC). Unlike existing methods that inject uniform random noise into all agents and use trigger functions based on absolute state information, we propose a method that customizes noise injection according to agent importance with an adaptive trigger function to dynamically adjusts the communication frequency using the relative state information of agents, thereby reducing unnecessary transmissions while maintaining effective consensus. Experimental results verify the effectiveness of the proposed algorithm.
AB - Frequent (agent-state) information exchanges for consensus in a multi-agent system (MAS) increases not only communication overhead but also the risk of sensitive information disclosure. This paper addresses the challenge of how to enhance data privacy protection by reducing information exchanges in MAS consensus based on event-trigger control (ETC). Unlike existing methods that inject uniform random noise into all agents and use trigger functions based on absolute state information, we propose a method that customizes noise injection according to agent importance with an adaptive trigger function to dynamically adjusts the communication frequency using the relative state information of agents, thereby reducing unnecessary transmissions while maintaining effective consensus. Experimental results verify the effectiveness of the proposed algorithm.
KW - Differential privacy
KW - Event-triggered mechanism
KW - Multi-agent systems
UR - https://www.scopus.com/pages/publications/105048223108
U2 - 10.1007/978-981-95-9846-5_16
DO - 10.1007/978-981-95-9846-5_16
M3 - Conference contribution
AN - SCOPUS:105048223108
SN - 9789819598458
T3 - Lecture Notes in Computer Science
SP - 177
EP - 187
BT - Parallel and Distributed Computing, Applications and Technologies - 26th International Conference, PDCAT 2025, Proceedings
A2 - Tian, Hui
A2 - Park, James Jong Hyuk
A2 - Zhang, Yong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2025
Y2 - 22 November 2025 through 24 November 2025
ER -