Differentially Private Distributed Optimization With an Event-Triggered Mechanism

Enthalten in
IEEE Transactions on Circuits and Systems I: Regular Papers 
Volume
70
Issue
7
Start Page
2943
End Page
2956
Citation
IEEE Transactions on Circuits and Systems 70 (7): 2943-2956 (2023-07)
Publisher DOI
10.1109/TCSI.2023.3266358
Scopus ID
2-s2.0-85153800800
ISSN
15498328
This 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.
Schlagworte
Convergence
Cost function
differential privacy
Differential privacy
Distributed optimization
event-triggered mechanism
Information exchange
Privacy
Real-time systems
Standards