FedJAX
A JAX-based library for federated learning simulations that emphasizes ease-of-use in research.
What is FedJAX?
FedJAX is a JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research. It provides simple primitives for implementing federated learning algorithms along with prepackaged datasets, models, and algorithms to accelerate research in distributed machine learning. The library enables fast simulation speeds on accelerators like GPUs and TPUs without requiring significant additional effort.
Machine learning researchers and practitioners working on federated learning algorithms who need a flexible simulation framework for experimentation and evaluation. It's particularly valuable for those who want to leverage JAX's automatic differentiation and XLA compilation capabilities.
FedJAX offers a research-focused design with simple primitives that make implementing and testing federated algorithms faster compared to building simulations from scratch. Its integration with JAX provides automatic performance benefits on accelerators, while prepackaged components reduce setup time for common federated learning scenarios.
Overview
FedJAX is a JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.
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