fedjax
by google
FedJAX is a JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.
AI summary
Federated learner simulator
A library that provides an easy-to-use framework for simulating federated learning algorithms
- stars
- 254
- forks
- 41
- watching
- 11
Similar projects
Found by comparing what the projects do, not just their names.
Federated learning model
A Python implementation of Personalized Federated Learning with Graph using PyTorch.
Federated learner
A federated learning framework with discrepancy-aware collaboration for decentralized data training
Federated Learning Algorithm
An unsupervised federated learning algorithm that uses cross knowledge distillation to learn meaningful data representations from local and global levels.
Federated Learning Framework
A framework for collaborative distributed machine learning in enterprise environments.
Federated Learning Toolkit
Provides tools and APIs for designing, scheduling, and running federated machine learning jobs in a secure and efficient manner.
Federated Learning Platform
A comprehensive platform for federated learning, providing an event-driven architecture and flexible customization for various tasks in academia and industry.
Federated Learning
This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets.
Data Fusion Tool
This project enables federated learning across partially class-disjoint data with curated bilateral curation.
Federated Learning Framework
Evaluates various methods for federated learning on different models and tasks.
Federated learning library
An implementation of federated learning with prototype-based methods across heterogeneous clients
Federated Learner
An implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships
Federated Learning Library
An implementation of Personalized Federated Learning with Gaussian Processes using Python.
Federated learning framework
Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.
Federated Learning Simulator
Simulates a federated learning setting to preserve individual data privacy
Federated learning server
A high-performance serving system for federated learning models, providing support for online algorithms, real-time inference, and model management.