Scalable and Probabilistic Leaderless BFT Consensus through Metastability
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Abstract (arXiv)
This paper introduces a family of leaderless Byzantine fault tolerance protocols, built around a metastable mechanism via network subsampling. These protocols provide a strong probabilistic safety guarantee in the presence of Byzantine adversaries while their concurrent and leaderless nature enables them to achieve high throughput and scalability. Unlike blockchains that rely on proof-of-work, they are quiescent and green. Unlike traditional consensus protocols where one or more nodes typically process linear bits in the number of total nodes per decision, no node processes more than logarithmic bits. It does not require accurate knowledge of all participants and exposes new possible tradeoffs and improvements in safety and liveness for building consensus protocols. The paper describes the Snow protocol family, analyzes its guarantees, and describes how it can be used to construct the core of an internet-scale electronic payment system called Avalanche, which is evaluated in a large scale deployment. Experiments demonstrate that the system can achieve high throughput (3400 tps), provide low confirmation latency (1.35 sec), and scale well compared to existing systems that deliver similar functionality. For our implementation and setup, the bottleneck of the system is in transaction verification.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2019.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2024Consensus learning: A novel decentralised ensemble learning paradigmcited by 0
- 2025Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approachcited by 0
- 2025Fortytwo: Swarm Inference with Peer-Ranked Consensuscited by 0
- 2025Snowveil: A Framework for Decentralised Preference Discoverycited by 0
- 2025Snowveil: A Framework for Decentralised Preference Discovery20 Dec 2025
- 2025Fortytwo: Swarm Inference with Peer-Ranked Consensus27 Oct 2025
- 2025Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach24 Feb 2025
- 2024Consensus learning: A novel decentralised ensemble learning paradigm25 Feb 2024