Event-Based Privacy Preserving Resilient Leader-Follower Consensus Control against Cyber-Attacks in Multi-Agent Cyber-Physical Systems
Bibliographic record
Abstract
This article addresses the event-triggered privacy preserving leader-follower consensus control problem for linear Multi-Agent Cyber-Physical Systems (MACPS) subject to cyber-attacks. A novel framework is proposed that combines virtual dynamics, event-triggered communication, and cyber-attack estimation to achieve output consensus while preserving agent privacy and system resilience against cyber-attacks. By introducing event-triggered virtual nodes, we enhance privacy of the agents while also managing communication resources efficiently by reducing data transmissions. To mitigate the effects of cyber-attacks, auxiliary systems are employed to estimate cyber-attack signals, enabling the design of a resilient control law to achieve leader-follower consensus in a Uniformly Ultimately Bounded (UUB) sense. Finally, Simulation results demonstrate the effectiveness of the proposed method in achieving leader-follower consensus despite cyber-attacks and communication limitations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".