Federated Learning: A World of Decentralized Data Science Tools and Libraries

Federated learning (FL) is an evolving landscape that revolves around learning from decentralized data. In essence, FL lets you train models across different devices or servers while holding the data locally, ensuring privacy. Below are the some of the libraries and tools to practice FL.

PySyft is a Python library that integrates with PyTorch, employing Federated Learning, Differential Privacy, and Secured Multi-Party Computation for private deep learning.

TensorFlow Federated serves as an open-source framework for computations and machine learning on decentralized data. For those looking for a similar but mobile-focused platform, Tensor/IO comes handy. Working with TensorFlow and TensorFlow Lite, it supports multiple languages.

Federated AI Technology Enabler (FATE) is an open-source initiative by Webank’s AI Department that supports vertical and horizontal federated learning and federated transfer learning.

The medical domain can benefit from Clara SDK developed by NVIDIA. It not only provides pre-trained models and datasets but also fosters Federated Learning.

Xaynet, another open-source framework for federated learning, operates across various devices, including mobile phones, desktop browsers, and edge devices. Written in Rust, it integrates seamlessly with platforms like Flutter.

Ichnite is a licensed platform by Intellegens, while FFL-ERL, built on Erlang, suits when the time to market outweighs performance. For encrypted data training, CrypTen—built on PyTorch and supporting Linux and MAC—is ideal.

LEAF and FedML act as benchmarking frameworks for learning in federated settings. LEAF has applications in federated learning, multitask learning, meta-learning, and on-device learning. On the other hand, FedML, developed by Alibaba Group, supports various machine learning tasks, offering scalability and efficiency.

FLlib and FedProx—both developed by the University of California, San Diego and Berkeley respectively—are designed to be privacy-preserving, scalable, and user-friendly, supporting various machine learning tasks.

Sherpa.ai Federated Learning Framework offers a suite of tools, allowing developers to train models across numerous devices without sharing raw data. In a similar vein, Flower is an open-source framework for federated learning, which aims to provide flexibility and high customization.

PaddleFL is an open-source framework developed by PaddlePaddle, focusing on usability, scalability, and privacy-preserving for federated learning.

Lastly, OpenFL, a collaborative initiative from the Intel AI Lab, aims to make federated learning more accessible to the open-source community. It supports TensorFlow, PyTorch, and other popular frameworks.

Federated Learning is increasingly becoming a key approach to maintain data privacy while enabling machine learning models to learn from a large pool of data. These tools and libraries are at the forefront of this movement, each with their unique features and capabilities. Depending on your needs and the specific requirements of your project, you can choose a framework that best suits your goals.

Connect me at LinkedIn