FedScale VS FATE

Compare FedScale vs FATE and see what are their differences.

FedScale

FedScale is a scalable and extensible open-source federated learning (FL) platform. (by SymbioticLab)

FATE

An Industrial Grade Federated Learning Framework (by FederatedAI)
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FedScale FATE
4 2
366 5,498
3.0% 1.9%
7.9 9.9
4 months ago about 2 months ago
Python Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

FedScale

Posts with mentions or reviews of FedScale. We have used some of these posts to build our list of alternatives and similar projects.

FATE

Posts with mentions or reviews of FATE. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-17.

What are some alternatives?

When comparing FedScale and FATE you can also consider the following projects:

flower - Flower: A Friendly Federated Learning Framework

FederatedScope - An easy-to-use federated learning platform

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

fedjax - FedJAX is a JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.

openfl - The Open Flash Library for creative expression on the web, desktop, mobile and consoles.

ORBIT-Dataset - The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.

datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

openfl - An open framework for Federated Learning.

breaching - Breaching privacy in federated learning scenarios for vision and text