You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper presents a novel leaderless protocol (FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures) with a low communicational complexity and which allows a set of nodes to come to a consensus on a value of a single bit. The paper makes the assumption that part of the nodes are , and are thus controlled by an adversary who intends to either delay the consensus, or break it. This defines that at least a couple of honest nodes come to different conclusions. We prove that, nevertheless, the protocol works with high probability when its parameters are suitably chosen. Along this the paper also provides explicit estimates on the probability that the protocol finalizes in the consensus state in a given time. This protocol could be applied to reaching consensus in decentralized cryptocurrency systems. A special feature of it is that it makes use of a sequence of random numbers which are either provided by a trusted source or generated by the nodes themselves using some decentralized random number generating protocol. This increases the overall trustworthiness of the infrastructure. A core contribution of the paper is that it uses a consensus to obtain a consensus on the value of a bit, and which can relate to the validity of a transaction.

Cited Abstract(s):
[1]: 

[2]: 

[3]: Abstract A consensus protocol enables a system of n asynchronous processes, some of them faulty, to reach agreement. Both the processes and the message system are capable of cooperating to prevent the correct processes from reaching decision. A protocol is t -resilient if in the presence of up to t faulty processes it reaches agreement with probability 1. Byzantine processes are faulty processes that can deviate arbitrarily from the protocol; Fail-Stop processes can just stop participating in it. In a recent paper, t -resilient randomized consensus protocols were presented for t n 5 . We improve this to t n 3 , thus matching the known lower bound on the number of correct processes necessary for consensus. The protocol uses a general technique in which the behavior of the Byzantine processes is restricted by the use of a broadcast protocol that filters some of the messages. The apparent behavior of the Byzantine processes, filtered by the broadcast protocol, is similar to that of Fail-Stop processes. Plugging the broadcast protocol as a communicating primitive into an agreement protocol for Fail-Stop processes gives the result. This technique, of using broadcast protocols to reduce the power of the faulty processes and then using them as communication primitives in algorithms designed for weaker failure models, was used succesfully in other contexts.

[4]: This paper is on the consensus problem in asynchronous distributed systems where (up to f) processes (among n) can exhibit a Byzantine behavior, i.e., can deviate arbitrarily from their specification. One way to solve the consensus problem in such a context consists of enriching the system with additional oracles that are powerful enough to cope with the uncertainty and unpredictability created by the combined effect of Byzantine behavior and asynchrony. This paper presents two kinds of Byzantine asynchronous consensus protocols using two types of oracles, namely, a common coin that provides processes with random values and a failure detector oracle. Both allow the processes to decide in one communication step in favorable circumstances. The first is a randomized protocol for an oblivious scheduler model that assumes n > 6f. The second one is a failure detector-based protocol that assumes n > tif. These protocols are designed to be particularly simple and efficient in terms of communication steps, the number of messages they generate in each step, and the size of messages. So, although they are not optimal in the number of Byzantine processes that can be tolerated, they are particularly efficient when we consider the number of communication steps they require to decide and the number and size of the messages they use. In that sense, they are practically appealing.

[5]: We present a correctness proof for Ben-Or''s Randomized Consensus Algorithm for the case in which processes can fail by crashing, and a majority of processes is correct. This is the first time that the proof of Ben-Or''s algorithm appears for this case. The proof has been extracted from [AT96]: it is a simplification of the correctness proof of a more complex consensus algorithm that involves both randomization and failure detection.

[6]: We present a randomized solution for the Byzantine Generals Problems. The solution works in the synchronous as well as the asynchronous case and produces Byzantine Agreement within a fixed small expected number of computational rounds, independent of the number n of processes and the bound t on the number of faulty processes. The solution uses A. Shamir's method for sharing secrets. It specializes to produce a simple solution for the Distributed Commit problem.
There is a wide range of classical work on (probabilistic) Byzantine consensus protocols [5] [2] [3] [1] [4] [6] . The disadvantage of the approach of these papers is, however, that they typically require that the nodes exchange @math messages in each round (which means @math messages for each node). In the situation where the communicational complexity matters, this can be a major barrier.