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Differentially Private Distributed Optimization With an Event-Triggered Mechanism
- Source :
- Circuits and Systems I: Regular Papers, IEEE Transactions on; 2023, Vol. 70 Issue: 7 p2943-2956, 14p
- Publication Year :
- 2023
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 15498328 and 15580806
- Volume :
- 70
- Issue :
- 7
- Database :
- Supplemental Index
- Journal :
- Circuits and Systems I: Regular Papers, IEEE Transactions on
- Publication Type :
- Periodical
- Accession number :
- ejs63411346
- Full Text :
- https://doi.org/10.1109/TCSI.2023.3266358