The byzantine general’s problem is an important concept in distributed networks and is the core principle underpinning distributed consensus systems.

So what is the general's problem? It is a game theory problem that shows how difficult it can be for dispersed parties to reach an agreement (consensus) without the help of a trusted central party or intermediary. The problem goes as follows:

"Imagine an ancient city at war with Byzantium.

The city is approached from all sides by 4 divisions of the Byzantine army. Each division has a general with unitary control over its division. The city is well defended; it will require the entire Byzantine army to capture it. If just one division attacks, they will surely be defeated.

Once the generals arrive with their armies, they must make a choice: fight or retreat. Remember, the generals are dispersed evenly from all sides, but it is important that they act in unison.

Because this is ancient times, the generals have limited communication. All they can do is write a message on paper, give it to a messenger and send them to other generals."

Anonymity and Asynchronicity The communication among the generals is asynchronous, bidirectional, and uncoordinated. In an otherwise perfect world, we could elect a general leader and send out orders, but this isn't a perfect world.

Remember, the Byzantines are attacking a city that presumably doesn't want to be conquered.

The city's people may keep a close eye out for the messengers moving between generals. What if they are able to capture a messenger and stop the message? Or what if they capture the messenger and REPLACE the message?

Maybe the city has seen this invasion coming for months and has been planning to sabotage the entire time. What if they were able to turn one of the Byzantine generals?

The core of the Problem This is the core of the Byzantine Generals Problem: how can members of a network agree on a specific reality when no one can verify the identities of other members?

As such, Byzantine Fault describes a system with components that may fail but does not provide clear, reliable data on whether the component has failed. A message being replaced by the city defenders or a traitorous general lying are examples of Byzantine faults.

Byzantine Fault Tolerance A system is Byzantine Fault Tolerant (BFT)if it is capable of resisting these classes of failures (and attacks). BFT systems (generally) try to optimize for two properties:

Safety - all honest participants can agree on the sequence of events and therefore have the same information

Liveness - the system must be able to come to a consensus and progress forward eventually.

Public Layer 1 Blockchains (Bitcoin, Ethereum, Solana, Avalanche, Fantom) have their own modes of achieving consensus, to help negate the problems associated with BFT. So when you are researching blockchains, their mode of consensus is an important feature to understand