The linfa project provides a machine learning framework written in Rust.
FATE
FATE supports federated learning through an industrial grade framework.

About FATE
FATE (Federated AI Technology Enabler) is an open source framework for federated learning intended to let enterprises and institutions collaborate on data with attention to data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC) and includes federated logistic regression, tree-based algorithms, deep learning, and transfer learning.
It can be installed on a single node via PyPI, pre-built Docker images, or installers for testing, or on multiple nodes via CLI or Docker Compose for scalability, reliability, and manageability.
Key features
- Federated logistic regression and tree-based algorithms
- Federated deep learning and transfer learning support
- Secure computation with homomorphic encryption and MPC
- Single-node install via PyPI, Docker images, or installers
- Multi-node cluster deployment via CLI or Docker Compose
- Training demos and Pipeline examples including Homo mode
- Open source project hosted by the Linux Foundation
- Includes federated logistic regression, tree-based, deep learning, and transfer learning algorithms
- Supports single-node and multi-node deployment methods
- Single-node deployment is described for simple testing purposes
FATE at a glance
- Website
- github.com/FederatedAI/FATE
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
FATE alternatives
Tools and sites you could use instead, ranked by how closely they match.
PaddlePaddle provides a deep learning framework for training models across devices and platforms.
The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.
MLX provides an array computing framework designed for Apple silicon devices.
QKeras provides quantization tools for deep learning with TensorFlow Keras.
Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.
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