Differentially Private Average Consensus With Logarithmic Dynamic Encoding-Decoding Scheme

This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized durin...

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Vydané v:IEEE transactions on cybernetics Ročník 53; číslo 10; s. 1 - 12
Hlavní autori: Chen, Wei, Wang, Zidong, Hu, Jun, Liu, Guo-Ping
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Chebyshev approximation
Coding
Communication
Convergence
Data transmission
Decoding
Differential private average consensus
dynamic encoding–decoding scheme
Eigenvalues
Heuristic algorithms
Indexes
logarithmic quantization
Logarithms
Multiagent systems
multiagent systems (MASs)
Optimization
Privacy
Probability theory
Quantization (signal)
Stability analysis
Stability criteria
Topology
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and <inline-formula> <tex-math notation="LaTeX">\epsilon</tex-math> </inline-formula>-differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3233296