FairDAG: Consensus Fairness over Multi-Proposer Causal Design
Summary: FairDAG moves consensus fairness from leader-based chains to multi-proposer DAGs, limiting proposer ordering manipulation while improving throughput. AB and RL variants outperform Pompe and Themis in throughput and fairness quality. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Dakai Kang (University of California Davis)
- 2. Junchao Chen (University of California Davis)
- 3. Tien Tuan Anh Dinh (Deakin University; University of California Davis)
- 4. Mohammad Sadoghi (University of California Davis)
BibTeX Citation
@article{kang_vldb26,
title = {{FairDAG: Consensus Fairness over Multi-Proposer Causal Design}},
author = {Kang, Dakai and Chen, Junchao and Dinh, Tien Tuan Anh and Sadoghi, Mohammad},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {2},
pages = {265--278},
doi = {10.14778/3773749.3773763},
url = {https://doi.org/10.14778/3773749.3773763},
year = {2026}
}
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