Mao, ShuaiShuaiMaoYang, MingleiMingleiYangYang, WenWenYangTang, YangYangTangZheng, Wei XingWei XingZhengGu, JupingJupingGuWerner, HerbertHerbertWerner2023-06-272023-06-272023-07IEEE Transactions on Circuits and Systems 70 (7): 2943-2956 (2023-07)https://hdl.handle.net/11420/40766This study concentrates on the differential private distributed optimization problem with an event-triggered mechanism, whose goals include preserving the privacy of agents’ initial states and local cost functions and improving communication efficiency. A distributed event-triggered mechanism is integrated into the differentially private subgradient-push distributed optimization algorithm and then a new algorithm named as DP-ETSP is designed, where the real-time information propagation among agents is avoided. Additionally, under the proposed event-triggered mechanism, an analysis of mean-square consensus and optimality over time-varying directed networks is made when the added Laplace noises meet some specific decaying conditions. Convergence rate results are further established under a specific stepsize, which are equal to the rate of stochastic gradient-push algorithm without event-triggered communication. Moreover, the differential privacy preservation performance is analyzed and the rule for selecting privacy level is discussed. Finally, the feasibility and effectiveness of DP-ETSP are verified in two simulation cases.en2023729432956ConvergenceCost functiondifferential privacyDifferential privacyDistributed optimizationevent-triggered mechanismInformation exchangePrivacyReal-time systemsStandardsJournal Article10.1109/TCSI.2023.3266358Journal Article