1234 points by deepmind_ai 7 months ago flag hide 15 comments
deeplearningdan 7 months ago next
This is a really innovative approach to training neural networks with differential privacy. Thanks for sharing!
mathmage 7 months ago next
I've been working on similar projects and I can confirm that this method works well in practice. It's definitely a game changer.
codedude 7 months ago prev next
I'm excited to see where this technique will be applied. Maybe it can be used to train models on sensitive medical data?
statsgal 7 months ago next
I agree, differential privacy is a powerful tool for protecting user data. I hope this technique becomes widely adopted in the industry.
neuronninja 7 months ago prev next
I'm curious how this compares to traditional training methods. Has there been any benchmarking conducted?
mlmonk 7 months ago next
Yes, there have been some studies done on this topic and the results are very promising. Here's a link to a recent paper: <https://arxiv.org/abs/XXXXXX>
aiadvocate 7 months ago next
I'm really impressed by this research. It's great to see the community pushing the boundaries of what's possible with differential privacy.
datascientist 7 months ago prev next
I've been trying to implement differential privacy in my projects but I've found it to be very challenging. Do you have any tips for getting started?
privacypro 7 months ago next
Yes, I recommend checking out Google's TensorFlow Privacy library. It's very user-friendly and has a lot of helpful documentation and examples.
codecrusader 7 months ago next
Thanks for the recommendation! I've been looking for a good library to use.
aiapprentice 7 months ago prev next
Thanks for the suggestion, I'll definitely check it out! I'm still a bit confused about the trade-offs between accuracy and privacy. Can anyone explain this better?
dlguru 7 months ago next
In general, there is a trade-off between privacy and accuracy, but recent research has shown that this doesn't have to be a zero-sum game. With the right techniques and hyperparameters, you can achieve both privacy and high accuracy.
mathmadman 7 months ago next
I recently read a paper on this topic and the authors suggested using a different optimizer to balance the trade-off. This could be a promising avenue for further research.
datadetective 7 months ago next
That's an interesting point. I'll have to read up on that paper and see what they suggest.
mlmaster 7 months ago prev next
I've found that using a smaller learning rate and adding regularization can help improve accuracy while still maintaining privacy. It's a bit of a balancing act, but it's definitely possible.