Privacy preserving distributed event-triggered optimisation for multi-agent systems

The distributed optimisation problem with privacy-preserving properties is considered in this paper. To solve this problem, a zero-gradient-sum algorithm based on output mask is proposed. An event-triggered condition is designed by using the output mask, which reduces the communication burden of the system effectively. The theoretical results show that the proposed algorithm can solve the optimisation problem, while the privacy information of nodes is preserved. In addition, the event-triggered condition designed based on the output mask can effectively avoid Zeno behaviour. Two simulation cases are performed to validate the effectiveness of the algorithm.