Data-Driven Event-Triggered Bipartite Consensus for Multi-Agent Systems Preventing DoS Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper considers event-triggered bipartite consensus issues for discrete-time nonlinear networked multi-agent systems with antagonistic interactions and denial-of-service (DoS) attacks. Firstly, a pseudo partial derivative technology is applied to obtain an equivalent dynamic linearization model of the controlled system. The signed graph theory is employed to analyze the coopetition relationships among agents. Next, a distributed combined measurement error function is formulated to transform the bipartite consensus issue into a consensus issue. Then, an output predictive compensation scheme is proposed to offset the influence of DoS attacks. Furthermore, a dead-zone operator is designed to improve the flexibility of the proposed event-triggered mechanism. Additionally, a data-driven event-triggered resilient bipartite consensus scheme is formulated. Then, the convergence of the proposed method is strictly proved by using the Lyapunov theory and the contraction mapping principle, which indicates that the bipartite consensus error could be cut to a small region around zero. Finally, hardware tasks are conducted to verify the effectiveness of the proposed method.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it