robot-arm VS flower

Modern robotics with deep learning/imitation learning is surprisingly accessible. The low-cost robot arm I used in this project is very easy to 3D print and assemble: https://github.com/trzy/robot-arm

An iPhone app is used to teleoperate the arm and gather examples of an action. You then train the model and deploy it and the arm performs the actions based on current camera input and joint angle state.

Flower: An open-source framework developed by Adap, which allows you to build federated learning systems using a variety of machine learning libraries.Link

We use Pyenv successfully for developing the Flower open-source project. We use a few simple Bash scripts to manage virtual environments with different Python versions via pyenv and the pyenv-virtualenv plugin.

The main scripts are `venv-create.sh`, `venv-delete.sh` and `bootstrap.sh`. `venv-reset.sh` pulls these three scripts together to make reinstalling your venv a single command.

Here's the link if anyone is interested: https://github.com/adap/flower/tree/main/dev

Flower , an open-source framework for training AI on distributed data. We move the model to the data instead of moving the data to the model. (https://flower.dev/)

Check out https://flower.dev/ as an example

Large language models like GPT-3 have gained immense popularity recently, and, using Flower, it's easy to transform an existing Hugging Face workflow to train models on decentralized data. This example blog post will show how to fine-tune a pre-trained distilBERT model on the IMDB dataset for sequence classification (determining if a movie review is positive or not). You can also check out the associated Colab notebook and the code example from the Flower repo.

There are some similarities, but also some differences. Flower's take is that it wants to support the entire FL workflow from experimental research to large-scale production deployments and operation. Some other FL frameworks fall either in the "research" or "production deployment" bucket, but few have good support for both.

Flower does a lot under the hood to support these different usage scenarios: it has both a networked engine (gRPC, experimental support for REST, and the possibility to "bring your own communication stack") and a simulation engine to support both real deployment on edge devices/server and simulation of large-scale federations on single machines or compute clusters.

This is - to the best of our knowledge - one of the drivers of our large and active community. The community is very collaborative and there are many downstream projects in the ecosystem that build on top of Flower (GitHub lists 748 dependent projects: https://github.com/adap/flower/network/dependents).

I agree that proprietary data will become more valuable. It is, even today, mostly not accessible for AI training and holds so much value. We are working on Flower (https://flower.dev), which enables training AI on private data without the data owner having to share it.

Flower framework: https://flower.dev/

The traction of federated learning is increasing as well as for our open-source federated learning framework Flower (https://flower.dev/). In federated learning, we do not collect data to train AI models but we train AI models in data silos, only collect the AI models and aggregate them to create a global AI model. The global AI model has the knowledge of all data silos but has never seen their data. Therefore, federated learning connects data silos in a privacy-preserving manner.