What is Byzantine Fault Tolerance?

Byzantine Fault Tolerance

Consensus models are a vital element of decentralized blockchain systems and unquestionably one of the most crucial to their functionality. They serve as the foundation for users to communicate with one another in an unreliable way. Their effective integration into bitcoin platforms has given rise to a wide range of networks with fantastic potential. However, Byzantine Fault Tolerance occurs in distributed systems. The capacity of a distributed computer network to maintain intended functionality and correctly obtain a satisfactory consensus despite malevolent system components (nodes) malfunctioning or spreading false information to other peers is known as tolerance.

The decentralized nature of cryptocurrencies is one of their distinctive features. Because transactions are verified and recorded by a distributed network of nodes, cryptocurrencies can function without being overseen by a single entity. This usually happens with inefficient financial analysis. Each cryptocurrency needs a method of reaching consensus for this to function. We can use Byzantine Fault Tolerance in this situation.

Byzantine Fault Tolerance is among the most significant aspects of cryptography, despite not being the most often addressed. Here is all the information you require on what it is and why it is crucial.

In this article, we will discuss the following:

  • What is Byzantine Fault Tolerance?
  • Assumptions about Byzantine Fault Tolerance
  • Importance of Byzantine Fault Tolerance
  • How does blockchain correlate to Byzantine Fault Tolerance?
  • What does Byzantine Fault Tolerance Practically Mean?
  • Byzantine Fault Tolerance: Advantages and Disadvantages
  • Limitations

What is Byzantine Fault Tolerance?

Byzantine Mistake A computer system’s tolerance is its capacity to keep running even if some of its nodes malfunction or behave maliciously.

It derives the phrase from a conjecture known as the Byzantine Generals Problem. As one might imagine, a group of Byzantine field commanders is at the center of this logical problem. The generals must collectively decide whether to attack or retreat since each has an army and a position surrounding a stronghold. They will be successful if they all choose the same thing. However, if a communication error or act of treachery results in some generals attacking while others evacuate, the fight is lost. Byzantine faults are this category of issues.

We can regard each node in a computer system with several nodes as universal. Byzantine Fault Tolerance measures a system’s ability to continue operating if specific nodes fail or purposefully try to fool it. The PBT model assumes that there are autonomous node failures and manipulated data transmitted by specific, unrelated nodes to provide a viable Byzantine state machine reproduction that tolerates Byzantine faults (malignant nodes).

The approach is high-performance, has an excellent overhead runtime, and slightly increases latency. In addition, it intends to operate in asynchronous systems. The PBT model’s nodes arrange essentially in a series, with one as the major node (leader) and the rest as backup nodes.

The objective is for all honest nodes to reach a consensus on the system’s state. It happens through a majority of the nodes communicating with one another. Nodes communicate with one another frequently, and they must not only demonstrate that messages originated from a particular peer node but also confirm that someone did not alter the information in transit.


The PBT model requires that, during a specific window of vulnerability, the number of potentially malicious nodes in the network cannot exceed concurrently equal or surpass one-third of all nodes in the system. The more nodes there are in the system, the more mathematically improbable it is that 13 or more of them will be evil. As long as at most (n-1) / 13 of the total nodes, where n stands for the total number of nodes, are malicious or flawed simultaneously, the method successfully maintains both liveness and safety.

As a result, clients finally receive accurate responses to their requests due to linearizability. Four steps are in each round of pBFT consensus (referred to as views). Due to the existence of a leader node, this model resembles a “Commander and Lieutenant” style more than a pure Byzantine Generals’ Problem, where all generals are equal. Here are the phases:

  • To request the leader node to do a service operation, a client must do so.
  • The request is broadcast to the backup nodes by the leader node.
  • The request completes the nodes, who subsequently reply to the client.
  • The client waits for f + 1 (where f is the total number of potentially flawed nodes) responses from various nodes with the same outcome. The operation produced this result.

Deterministic nodes begin in the same state as the nodes. All honest nodes ultimately agree upon the order of the database, and they can then choose to recognize it or reject it. Every time they take a view, the leader node switches round-robin. If a predetermined period has passed without the leader node transmitting and receiving the request, it may even replace the leader node with the view change protocol. A leader may also be removed and replaced by the following leader in line if they find it defective by a majority in both houses of honest nodes.


Byzantine Fault Tolerance is significant from a computing standpoint because it ensures that a system can continue to run even if some of its components fail. Anything that employs a computing system, like an aircraft or a spacecraft, must function even when some nodes are not operating at total capacity. There’s another benefit to understanding this topic if you invest in cryptocurrency-related stocks or particular coins. 

How does Blockchain Correlate to Byzantine Fault Tolerance?

Cryptocurrencies use blockchain technology to confirm, perform, and record transactions. A collection of nodes must concur to carry out a valid transaction. Consensus algorithms are the guidelines that each blockchain network’s nodes adhere to agree on transactions.

The consensus mechanism accomplishes Byzantine Fault Tolerance in a blockchain. Due to the decentralized nature of cryptocurrencies, each one encounters a severe case of the Byzantine General’s Problem. The blockchain must be able to continue operating even if any of its nodes are malfunctioning or giving inaccurate information.

With proof of work, Bitcoin (CRYPTO: BTC) initially attempted to address this issue. However, to use this consensus technique, miners must use specialized computer hardware to solve complex equations. The privilege of adding a set of transactions and receiving rewards belongs to the first miner to correctly answer an equation. If you don’t produce the correct data, you’ve wasted time and effort.

Proof of stake is another common variety of consensus algorithms. Validators must stake their cryptocurrency or lock it in a wallet in exchange for the privilege of confirming transactions. You can receive cryptocurrency incentives if the protocol chooses you to contribute a block to the blockchain. However, approving incorrect transactions will result in losing part or all of your staked cryptocurrency. Due to their ability to continue functioning even while some nodes supply inaccurate data, each consensus algorithm exhibits some degree of Byzantine Fault Tolerance.

What does Byzantine Fault Tolerance Practically Mean?

A system with practical byzantine fault tolerance has additional nodes and a central node. This system is one of the approaches to the Byzantine General’s Problem since these nodes cooperate in obtaining a consensus. Here is a brief explanation of how Byzantine Fault Tolerance in practice operates:

  • The primary node receives a request from the client.
  • This request is forwarded to the subsidiary nodes by the primary node.
  • The nodes handle request processing, service delivery, and client communication.
  • The client waits until m+1 nodes, where m is the maximum number of incorrect or malicious nodes that the system permits, have returned the same result.
  • In a real-world Byzantine Fault Tolerance system, the number of malicious/faulty nodes cannot exceed one-third of all nodes in the system.

Byzantine Fault Tolerance: Advantages and Disadvantages

Since no miners are working to solve complex equations for each block of transactions, it doesn’t require a lot of computational power or energy. Because of this, it is far more ecologically friendly than work-related proof. Multiple confirmations are not necessary for transactions. The block confirms if all nodes concur on a block of transactions.

Since all nodes can participate, they can all benefit from the rewards. As opposed to proof of labor and proof of stake, where there might be a wide variation in which nodes receive tips. There is no ideal or workable answer. There are some significant dangers with Byzantine Fault Tolerance as well:

It is susceptible to Sybil attacks, where one party can take control of a significant number of nodes (the name comes from a book about a lady with multiple personality disorders). It becomes increasingly difficult to mount a Sybil assault when there are more nodes. At each stage of the procedure, nodes must communicate with one another. So this takes time, which might be problematic in terms of scaling.

There will always be unscrupulous actors trying to manipulate transactions, and a blockchain may occasionally not have every node functioning properly. However, every cryptocurrency has a trust model built to support it in achieving Byzantine Fault Tolerance to some extent. As long as at least two-thirds of the nodes operate correctly, these algorithms often allow the coin to function normally.


Due to the substantial communication overhead that rises exponentially with each additional node in the network, the PBT consensus model only functions well when the number of nodes in the distributed network is low.

The PBT methods are vulnerable to Sybil attacks, in which one entity (party) controls numerous identities. Sybil attacks get harder to execute as the number of nodes in the network grows. But because pBFT procedures also have scaling problems, they are utilized in conjunction. Due to its communication overhead (with all the other nodes at every step), pBFT does not scale well. The time it takes to react to a request increases with the increase of nodes in the network, where n is the number of communications and k is the number of nodes) with other mechanisms.

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