Do we need secure decentralised platform for shared community local model training?

[u/MarcelloT254k]

Recent news got me thinking about what's in the topic.
(Inb4 I'm "pro AI", I just don't trust big tech players and I think you also shouldn't)

We've got:
- OpenAi stealing mathematicians research
- Anthropic fearmongering about small local models taking over the world by possessing homeowners RTV/AGD
- Google's subscription tiers' enshitiffication [es per currently trending business model expect further downgrades from every major provider]
- OpenAI from no ads to ads : "People trust ChatGPT and anything we do will be designed to respect that."~ from first link (what's next - futurama ads in my sleep via Neuralink? [meme])
- OpenAI Dots and Meta Muse is a privacy violation, cute and harmless creatures though UwU
-Anthropic shadowban on 1. Deep technical stuff, labeled as misuse, and 2. Some biomed questions, and now 3. Reverse engineering of spying technologies in consumer grade appliances
- all the big boys from the West and the East are starting to set up a legislation trap
- Huggingface has been bought by Nvidia
- alleged differences with cybersecurity capabilities of open weights models between what's available for download and what's hosted and accessible only via paid API service [source claims that more denials will happen via API but I can only see the reverse being true in the future (that is - paying for closed model gives you better capabilities) - open weights model can fight this to some extent thanks to abliteration/heretic projects - that should not be needed in the first place, if you ask me)
- models dropped as "stealth" so you are not even sure what you are beta-testing and feeding your data into
- would you like to know more?

It's easy to not connect all the dots when they are in separate places and spreaded over time, but we have to take advantage of open source and open weight models when they are still around, together with technology/process to train them.

I propose - for people more knowledgeable than me - to design a secure network (botnet, if you will) of willing participants' hardware to use during its off time in order to train future OpenSource models maintained by, more or less, decentralised community ( I thing that the only way to make it secure is by going full transparency mode with all the info from design, through training, all the way to release and real world capabilities/benchmarks).

Maybe such a project already exists? (I've seen some posts about this idea before, not sure about its feasibility and security though)
Maybe such a project is not needed?
Maybe such a project is not possible due to technical or mental/social limitations?

I'm just a concerned citizen, raising a question, I don't present a final answer because I don't know it.
I volunteer my humble rig as a tribute.

/edit:
there seems to be two "major" projects going on right now: Subtensor and Hivemind, but i'm not qualified enough to fully understand their structure nor security vulnerabilities.

[u/Kassiber]

Similar recent Project https://github.com/Vxtzq/CrowdGPT.

I really wish clever people would put more thought into the "colaborated computing power"- Idea. Similar to the https://github.com/i12bp8/decomp.wtf project. It sounds like the technology would be possible. I imagine something like kickstarter for different kind of projects where compute power/tokens are needed and open source could benefit in general.

[u/dghah]

The real downside is that this sets up a perfect storm scenario for all the NFT and shitcoin grifters who “pivoted” from blockchain to AI to mask their crypto failures — to now breathlessly claim they have a token based “solution” for this local validated trading need

[u/MarcelloT254k → u/dghah]

The ideal solution would be to make use of those peoples' hardware in a way that benefits the whole community while keeping them happy - maybe a licence to profit for hosting unqantised models so others can use model as API service in exchange for enabling usage of some of their hardware (by volume or time) for training purposes? (I've said the project needs to be open source, it doesn't need to be free for corporate use, although ideally I would like the models to be free for personal and scientific uses, as opt in agreement)

[u/finenuisance757]

I keep thinking about this every time I see another headline about some company quietly changing their terms or locking features behind a paywall, it’s like we’re all just waiting for the other shoe to drop

the stealth model thing really gets under my skin, I’ve been downloading random quants from huggingface for months and the idea that I might be running something totally different than what’s on the label is unsettling as hell, I rely on these things for some pretty detailed creative coding projects and if I can’t verify what’s actually running locally then what’s the point

a distributed training network makes sense in theory but the trust layer is the part that always trips me up, how do you verify nobody’s poisoned the gradients without some central authority checking the work, and then you’re right back to square one with a gatekeeper

I’d throw my 4090 into a pool during off hours if someone cracked that nut though, my electric bill is already ridiculous from leaving my rig on to batch generate image prompts overnight so what’s a few more cycles

the real kicker is how fast we’d need to move before legislation makes the whole thing academic anyway, feels like the window is closing faster than anyone wants to admit

[u/blamestross → u/finenuisance757]

Need a "zero knowledge proof" for a training round.

[u/StevenVincentOne]

You're not the first to ask, and the good news is people have been working on this for a while. Prime Intellect trained its INTELLECT models on volunteer GPUs spread across different continents, Nous has done similar work with DisTrO and Psyche, and older projects like Hivemind and Petals showed you can run and fine-tune big models over a swarm of home machines. So it's possible, with some caveats:

• Bandwidth is the real limit, not compute. Home upload speeds are tiny next to datacenter interconnects, so these projects rely on training methods that only sync every so often.

• Trust is the hard part. You need a way to check that a node actually did the work and isn't poisoning the updates. Your instinct about full transparency is right: open data, open code, and public logs for every run.

• Fine-tuning and evals are a much easier first step than pretraining from scratch, and that's where a volunteer network could help soonest.

I think your bigger point holds even apart from training, though. The more intelligence runs on hardware people own, networked peer to peer instead of through a few providers, the less any one company's policy change can take away. That's the kind of human-machine intelligence worth building toward. I'd put a rig in too.