Borderless Ethereum - Measuring the geographical distribution of validators

Borderless Ethereum is a volunteer-led initiative to try to measure the geographical and jurisdictional distribution of validators in Ethereum. In order for Ethereum to remain a credible neutral platform that is free from nation state coercion, it needs to have a wide geographical distribution of validator nodes. This is a difficult challenge to address, but in order to make any progress, we need to be able to measure where we are at, and to be able to measure the effect of any changes we make.

However, this is a challenge in itself. While we can use existing datasets to track the geographical distribution of nodes on the network, these are CL+EL clients, not validator nodes. Many nodes on the network do not have a Valdiator Client associated with them, think of all the nodes that companies like Infura, Alchemy etc. run. Furthermore, some Beacon Nodes may have several Validator Clients connected to them, and some may even have Validator Clients in separate locations.

So we need some way of tracking where the validator clients are, and in a way that does not reveal details about their location, in fact, we really only need to know which country they are in. To do this, we’re trying to get node operators to place a “geo tag” in the graffiti field of the beacon chain blocks they propose. The geo tag is simply GEO-XX where XX is the two letter country code for the country that the validator client is in. We will then scrape the graffiti fields from each block and count how many validators are in which country, and will try to track this over time.

We feel that this form of self-reporting of location can provide valuable information while also maintaining validator location privacy. In order to encourage participation, we will drop a POAP to the addresses of the validators that include the geo tag in their blocks.

We would very much value feedback and suggestions on this proposal, and would appreciate any help that people can give with raising awareness for the project, and hopefully increasing participation.

Original post: Geographical Decentralisation - Economics - Ethereum Research


Thanks for this idea and initiative @orbmis !

I think it makes sense, at least initially, for Node Operators to support this on a voluntary basis and then for a discussion to happen whether it makes sense for it to be something that should be applied across the set (difficulty of something like this is that perhaps some NOs don’t want to divulge this information directly, and if you make it a policy then you also need to enforce it).

From a personal perspective I think it would add valuable insight, and we could further compare results with analyzed crawler data from things like @leobago and team’s Eth2 Network Dashboard to do a rough reconciliation of how correct the self-reporting looks (we had an interesting discussion in yesterday’s community call about how we may be able to use heuristic to determine a rough approximate of which beacon nodes are connected to validators).

I look forward to feedback from Node Operators on this!

I think the part that we need to dig deeper a bit on is what we really mean by “validator location”. Because the components of a “validator node” are actually distinct, it’s possible for them to be in different places (and even for some of these components to have fallbacks in other places). To a certain extent, trying to distill all this information into one data point may be misleading and offer a more centralized view of how the network is laid out than is the case in reality.


Yes I agree, there is some confusion there with regards to what we mean by “validator location” which we’ve tried to address in the FAQs in the docs. Essentially we’re looking specifically for the country (NOT the actual location) of the Validator Client specially, (e.g. Vouch as opposed to Teku or Lighthouse). While there are other ways to do try to gather this data, we think this is the most non-intrusive and safest way to do it, and will reasonably test the assumption that the distribution of validator clients largely tracks the distribution of beacon chain nodes (which it may or may not).

I think starting no a voluntary basis is definitely the best way forward, and it would be great to see some support from some of the NOs and see some geo-tags appearing!


Glad to see this discussion revived from the original ethresearch thread!

I think that’s the right approach to start off with, especially focusing on collecting this data in a responsible, non-intrusive way. One thing that popped into my head that we might want to keep in mind is sample bias? Aka are the folks who are most likely to respond to a call for self-disclosure (for some reason) more likely to be located in specific regions? Maybe is a non-concern for the initial sampling :man_shrugging:

Might be more manageable from this POV to start off with the Lido NO set, take learnings from that exercise on methodology (with disclaimers on potential risks of drawing broad conclusions from the dataset), and then gradually expand to other staking communities outside of Lido?

What do you think?


Pretty cool, I’ll give a hand if you need me


Hey Alon,

Much appreciated thank you! It would be great to get a view on how this approach might compliment DVT, or if you think it’s just not applicable in the DVT context. It would be great to get your perspective.

One thing I’m starting to realise from the feedback I’m getting, is that node operators are hesitant to reveal any more data than is necessary about their location, even at the country level. One idea I had that might assuage any fears operators might have, is to introduce some sort of simple differential privacy using randomised responses. If we ask operators to flip a coin, and report truthfully if heads, or pick a random value if tails, we would still be able to get a reasonably accurate idea of which countries validators are located in without doxxing anyone. The actual probability may need to be different from 50/50 but you get the idea.


Nice idea @orbmis, geographical diversity is important indeed.

With the crawler we have at MigaLabs, we can get the country of most beacon nodes we connect with, but as @Izzy mentioned that does not tell us whether that node has validators connected to it or not. However, we also have other extra information. For instance, beacon nodes also share which attestation channels they are interested in. If you are a beacon node without validators, most likely you will avoid the extra work of listening and forwarding messages on more channels than the ones you really need. Beacon nodes attached to validators do need to listen to the attestation networks required to validate blocks according to their respective duties. So if we cross these two datasets, we can already have an idea of the geographical distribution of validators.

In addition to this, we can also look at things like whether the node is a cloud node or a residential node, which we can also derive with a relatively high degree of confidence.

What I would suggest is to implement both strategies in parallel and keep them independent, and then when we get some first results compare both distributions. I would be happy to have my team working on this.

What do you think?


Hey Leo,

I really like the idea of trying to track the beacon nodes that share the attestation channels they’re interested in. Do you think that would be data you would be interested in publishing on your dashboard? (the Miga Labs dashboard is very cool by the way!).

I also think that trying to measure the ratio of nodes in data centres as opposed to other locations is important, because it gives an idea of the jurisdictional distribution as well as the purely geographical distribution. This would be similar to what Ethernodes do for execution clients.

I think implementing both strategies is a terrific idea. I have a beacon chain graffiti scraper running and so if we assume that even some validators participate, then we can use it as a sample with a standard confidence interval and compare it to other datasets.


And within DC deployments, wonder if it’s possible to delineate between managed public cloud vs bare metal? I do think the distinction is meaningful here. might be able to chime in here too

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Interesting. Yes, I think that would be a good metric to track.


A great discussion, and I just want to point to another discussion that I started that you might want to look at: Execution & Consensus Client Bootnodes - Node Operators - Lido Governance. One thing I would like to point out is that the use of the Graffiti field on a voluntary basis can be used for psyops strategies: e.g. a cartel of validators wants to provoke the regulators of a certain X country and intentionally always puts this X country in the geo-tag. Furthermore, any kind of IP addresses or location information may have been previously obfuscated. So the reliability of any kind of such data is somehow correlated with the willingness to be honest.

Just a note on bare metal: The problem with cloud solutions is the following; most of them a running under US law, so in case we even diversify on the cloud providers for the bootnodes, a single point of failure exists: the US enforcement possibility. So that’s why I support bare-metal solutions very much.

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Those are very good points. I hadn’t thought about a cartel of validators wanting to provoke the regulators in a certain jurisdiction. Do you think this is something that’s likely? My idea is predicted upon the presumption that node operators don’t have any plausible incentive to be dishonest. However, what this discussion has surfaced is that there seems to more appetite for making it difficult to ascertain where validator clients are (maybe to the point that it doesn’t matter where they are located or concentrated, if nobody can effectively conclude or prove where they are).

I think the conversation you’ve started on boot nodes is hugely important, and I think it’s great that you’re making it visible. I’ll do my best to help out in that regard.

With regards to bare metals servers, correct me if I’m wrong I think it’s probably to point out that we’re talking about bare metals servers outside of large cloud data centres, (i.e. as opposed to provisioning a bare metal server AWS etc.).

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I don’t know what’s the probability of such a scenario, but what I know is that having such a psyops/manipulation option doesn’t make that approach future-proof since there might be unknown unknowns we’re not aware of it right now. Also, I like to remind about Goodhart’s law which states: “When a measure becomes a target, it ceases to be a good measure.” So in case the validator locations become a target, the measure ceases to be a good measure at all since it can be manipulated.

Example: One (possible) scenario is that Gary Gensler declares Ethereum a security, Bitcoin maxis acquire a large amount of ETH (they might sacrifice their sats for this attack ;-)), and become a large pool of validators. In order to increase the regulatory scrutiny of the SEC, they spam the graffiti with the US country tag. SEC gets nervous and declares staking and becoming a validator as illegal except if you have a specific security dealer’s license (or whatever specific license is required).

Thanks a lot for your support. Highly appreciated.

Well this can mean both: making use of bare metal servers of local DCs/cloud providers or actually maintaining your own bare metal servers (aka as home staking and/or home-hosted EL/CL clients). The latter requires a certain degree of hardware and software competencies and thus is not suited for everyone.

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I agree that the self-reporting approach may not be the most future proof, and leobago’s suggestion may be a better approach long term. However, I do think that the distribution of validators is something that should be measured. According to there are 561,655 active validators, and according to Miga Labs, there are 11,499 nodes. While I see the argument that if it’s impossible to ascertain where those validators are, it shouldn’t matter where they are, I think it’s dangerous to rely too heavily on that assumption. I still maintain that we would benefit from understanding if there is a significant concentration of validators in specific jurisdictions and taking steps to remediate if needs be.


I actually fully agree with this. My comments were more to highlight potential measurement failures.


Great discussion.

This month my team is quite busy with some deadlines but I think we can start working on this next month. I will send updates in this thread.


Awesome! I’m happy to help however I can - let me know!

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Hi @orbmis, I hope you are doing well.

My team and me have been working on this idea and we just released our new dashboards today:

You can see two options there, one to see the data about the number of beacon nodes in the network, and another one to see the number of validator nodes in the network. For the validator nodes, we filter them from the beacon nodes that are registered to at least one attestation network, which should imply that they are running at least one validator. Nodes that are not subscribed to any attestation network cannot have validators.

Take a look at it and let me know what you think! :smiley:


Hey @leobago - this is a really interesting approach, excellent work! From the data I see there’s about 2,080 nodes that are registered to at least one attestation network, and a total of 5,554 attestation networks (with 645,924 number of active validators, that works out at 116 validators per attestation subnet - I don’t know if that sounds right, I’ll try to double check). The distribution is very interesting, with the vast majority of validators registering to a single subnet, and the second largest cohort registering to 64 subnets. I would assume that is the two biggest groups of validators, i.e. solo takers and Lido respectively.

Out o curiosity: anyone could use potentially this method to track the IP addresses of nodes that have validator clients attached, correct?

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Hey @orbmis, thank you!

According to our data, there is about ~11.9K beacon nodes in the network, from which ~5.5K are subscribed to at least one attestation network (i.e., Validators). From this 5.5K, about ~1.9K are subscribed to only one attestation network, and about ~1.1K to 64 attestation networks, which is the total number of attestation networks that exist (64). So I would say there are about ~3K nodes running as solo stakers (less than 5 validators) and about ~1K nodes that belong to large institutional staking operators (e.g., Lido, etc).

To answer your other question: yes, anyone can use this method to track the IP addresses of all the nodes registered to an attestation network (which does not necessarily means all of them have validators), but you cannot use this method to track the IP address of any specific validator. In other words, we just have the list of nodes operating in the network and some partial information about them, but we don’t know who is who. Does this make sense?

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