EnvisEdge
privacy
| EnvisEdge | privacy | |
|---|---|---|
| 2 | 2 | |
| 135 | 1,876 | |
| - | 0.9% | |
| 3.5 | 7.8 | |
| 10 months ago | 19 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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EnvisEdge
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A new way to build decentralised recommendation engines for the creator economy
Hear me out on what I think a truly decentralised content curation.
Twitter, FB (Meta), Youtube everyone harvests user data and train their recommendation engines which are then monetised by them (often unfairly).
In the future, the data stays on the users' devices and anyone can train their models by asking the user for the consent. THe data never leaves the device and ML models get trained on user device itself. The users get to choose from a host of recommendation choices and can ask for payment in return for using their data. So no one party can build a monopoly over the platform.
Check out a cool project I have been working on to solve this https://github.com/NimbleEdge/RecoEdge
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Ask HN: What cutting-edge technology do you use?
Edge computing for machine learning. Instead of running ML models on the cloud, I train them on user's device, ask these devices to offload computation between each other and give me the best performance out there. I have my own local cloud formed by my laptop, smartphone and ipad.
I built out the library for these myself, checkout https://github.com/NimbleEdge/RecoEdge
privacy
What are some alternatives?
exodus - Platform to audit trackers used by Android application
differential-privacy - Google's differential privacy libraries.
Converter - Typescript to Scala.js converter
tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow
rtl-sdr-blog - Modified Osmocom drivers with enhancements for RTL-SDR Blog V3 and V4 units.
dp-xgboost
spotlight - Deep recommender models using PyTorch.
Differential-Privacy-Guide - Differential Privacy Guide
Quill - Compile-time Language Integrated Queries for Scala
adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
vision_ui - This is a vision-based 3d model manipulation and control UI
tf2-published-models - Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.