478 karma · joined October 1, 2013
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog order): Adam Narozniak, Charles Beauville, Edoardo, L. Jiang, Ragy, dannymcy
The new release is packed with improvements and new features: new Flower Baseline (FedAvg MNIST), improved GPU support in simulations, improved GPU support in Jupyter Notebook tutorials, optional telemetry, new (experimental) Driver API, new Federated Analytics with Pandas example, new strategies (Krum and MultiKrum), updated C++ example, a huge list of general improvements, and updated documentation. And of course: no incompatible changes
For more information, be sure to check our new blog post: https://flower.dev/blog/2023-01-13-announcing-flower-1.2
If this is happening all the time, then the number of global methane emissions due to human activity on this (https://en.wikipedia.org/wiki/Methane_emissions) Wikipedia page can't be valid.
Crazy how human negligence and greed might end humanity.
Waiting lists in chats? Seriously? I am not supposed to loose focus of the chat window? Holy cow.
You seem to be quite the expert on Norway. Can you elaborate how they prop up social services with oil money?
You can train models over multiple silos, devices, users and many other kind of partitioning where for some reason you can't aggregate the dataset centrally.
``` Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. ```
basically you can train ML models collaboratively without ever seeing the other datasets. One example would be multiple hospitals training models to detect breast cancer without the need to exchange the data samples.
Another example is how Google trains models for the keyboard on Android. See here: https://ai.googleblog.com/2017/04/federated-learning-collabo...
With that money people are usually able to get an apartment or at least a room in a shared apartment and pay for food and books.