Computationally Light Privacy Preservation of Matrix-Weighted Average Consensus

Multiagent consensus algorithms have emerged as foundational tools across a spectrum of applications, and matrix-weighted consensus ones are capable of characterizing cross-dimensional interdependence. Yet, their potential is often shadowed by a pressing concern: the privacy of agents' initial...

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Vydané v:	IEEE transactions on control of network systems Ročník 12; číslo 2; s. 1651 - 1661
Hlavní autori:	Wang, Peng, Shao, Haibin, Pan, Lulu, Yan, Weiwu, Li, Ning
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Consensus algorithm Data privacy Matrix-weighted average consensus (MAC) Multiagent systems Noise peer-to-peer (P2P) transactive energy systems (TESs) Peer-to-peer computing Perturbation methods Power supplies Privacy privacy preservation Transactive energy Vectors
ISSN:	2325-5870, 2372-2533
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