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Most companies dealing with deep learning (automotive - Comma.ai, Mobileye, various automakers etc.) do collect large amounts of data to learn from and then use lots of computational power to train a neural network (NN) from such big data. I guess this model is mainly used because both the big data and the training algorithms should remain secret/proprietary.

If I understand it correctly the problem with deep learning is that one needs to have:

  1. big data to learn from
  2. lots of hardware to train the neural network from this big data

I am trying to think how crowdsourcing could be used in this scenario. Is it possible to distribute the training of the NN to the crowd? I mean not to collect the big data to a central place but instead to do the training from local data on the user's hardware (in a distributed way). The result if this would be lots of trained NNs that would in the end be merged into one in a Committee of machines (CoM) way. Would such model be possible?

Of course the above stated model does have a significant drawback - one does not have control over the data that is used for learning (users could intentionally submit wrong/fake data that would lower the quality of the final CoM). This may be dealt with by sending random data samples to the central community server for review however.

Example: Think of a powerful smartphone using its camera to capture a road from vehicle's dashboard and using it for training lane detection. Every user would do the training himself/herself (possibly including any manual work like input image classification for supervised learning etc.).

I wonder it he model proposed above may be viable. Or is there a better model how to use crowdsourcing (user community) to deal with machine learning?

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First thing, you need to give more credit to more reliable users. You can establish the credit from amount of data they send, and a feature, where other users can review other's feed and classify it. From there, you will have a measure of certainty to what data is good and what is not.

You will need to implement a centralized server, unless you're trying to do some kind of a peer-to-peer trust systems, but I don't think smartphones are powerful enough to do training themselves.

You will need big machines for training NNets. Don't trust users to have them. You would end up with tons of badly trained NNets, which don't make for a good CoM.

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  • $\begingroup$ Excellent answer. Welcome to AI! $\endgroup$ – DukeZhou Sep 27 '17 at 19:10

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