pfl-research VS FedML

Compare pfl-research vs FedML and see what are their differences.

FedML

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale. (by FedML-AI)
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pfl-research FedML
1 6
193 4,068
23.4% 1.0%
8.4 9.9
4 days ago 6 days 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.

pfl-research

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

FedML

Posts with mentions or reviews of FedML. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-22.

What are some alternatives?

When comparing pfl-research and FedML you can also consider the following projects:

flower - Flower: A Friendly Federated Learning Framework

federated-xgboost - Federated gradient boosted decision tree learning

alpa - Training and serving large-scale neural networks with auto parallelization.

experta - Expert Systems for Python

HandyRL - HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.

adaptdl - Resource-adaptive cluster scheduler for deep learning training.

MetisFL - The first open Federated Learning framework implemented in C++ and Python.

hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

speech-to-text-benchmark - speech to text benchmark framework

auto-split - Auto-Split: A General Framework of Collaborative Edge-Cloud AI

openfl - An open framework for Federated Learning.

Federated-Learning-in-PyTorch - Handy PyTorch implementation of Federated Learning (for your painless research)