PySyft
openfl
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| PySyft | openfl | |
|---|---|---|
| 7 | 4 | |
| 9,253 | 658 | |
| 1.1% | 2.7% | |
| 10.0 | 8.5 | |
| 5 days ago | 4 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PySyft
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A Better Mastodon Client
https://github.com/OpenMined/PySyft - Federated Learning data science
Incentives are much harder but smart contracts can handle the tech part.
Going this route eventually you quickly have "quantum AI app store" and your system of government is a 12GB download. Can't even say if it's a good idea compared to e.g. anarcho-primitivism.
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Just about conspirancy theories... Can you say this guy isn't rigth?
Something that maybe can help keeping sensor specs secret while still getting critical information out: https://github.com/OpenMined/PySyft
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I made a YT video showing how to host your own super accurate (microsecond) network time (NTP) server using the PPS output of a $12 GPS module
Love this kind of project. To me this is just like https://github.com/open-quantum-safe/oqs-demos/ or https://github.com/OpenMined/PySyft or even k3s so often mentioned in this sub in the sense that I personally don't have a need for it. Yet I find it amazing that us, random curious geeks, have access to this kind of mind blowing technologies for basically free.
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Help on creating a Federated Recommender System
Or do I have to actually simulate the whole client server thing because thats how these frameworks do it - Flower and Pysyft .
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Integration test: Complexity of privacy-preserving bird call bio-sensor for distributed ecological monitoring?
Some of the technologies which could be integrated include differential privacy, distributed online machine learning, misinformation resilience and multi-party computation, all within the context of smart contracts and bioinformatics.
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Google Strikes Deal With Hospital Chain to Develop Healthcare Algorithms
I think this is how it will be done. Look up PySift for how we can extract high-level insights from private datasets while preserving granular privacy.
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Is it even possible to have a service as "intelligent" as Google while still being privacy respecting?
What you are talking about is privacy-focused fed ML. Google FLOC is actually trying to achieve something similar. If you are interested in building something for yourself, check this out. https://github.com/OpenMined/PySyft
openfl
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Dectralized AI solutions/building blocks?
We have petals.ml for distributing running a single model large model for inference. We have [flower](flower.dev),tensor flow federated,openfl, etc for building federated learning.
- Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
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[R][P] OpenFL: An open-source framework for Federated Learning
Code for https://arxiv.org/abs/2105.06413 found: https://github.com/intel/openfl
What are some alternatives?
fastai - The fastai deep learning library
FATE - An Industrial Grade Federated Learning Framework
openfl - The Open Flash Library for creative expression on the web, desktop, mobile and consoles.
fedjax - FedJAX is a JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.
AIDungeon - Infinite adventures await!
sparktorch - Train and run Pytorch models on Apache Spark.
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow
Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.
FindTheMag - A tool to determine optimal projects for Gridcoin crunchers. Maximize your magnitude!
unix-history-repo - Continuous Unix commit history from 1970 until today
FedML - FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.