Thank you for visiting our site. This documentation provides you with everything you need to know about using the FedML platform.
FedML, Inc. (https://fedml.ai) enables people and/or organizations to have AI capability from data anywhere at any scale. FedML stands for “Fundamental Ecosystem Development/Design for Machine Learning” in a broad scope, and “Federated Machine Learning” in a specific scope. At its current stage, FedML is developing and maintaining a machine learning platform that enables zero-code, lightweight, cross-platform, and provably secure federated learning and analytics. It enables machine learning from decentralized data at various users/silos/edge nodes without requiring data centralization to the cloud, thus providing maximum privacy and efficiency. It consists of a lightweight and cross-platform Edge AI SDK that is deployable over edge GPUs, smartphones, and IoT devices. Furthermore, it also provides a user-friendly MLOps platform to simplify decentralized machine learning and real-world deployment. FedML supports vertical solutions across a broad range of industries (healthcare, finance, insurance, smart cities, IoT, etc.) and applications (computer vision, natural language processing, data mining, and time-series forecasting). Its core technology is backed by over 3 years of cutting-edge research by its co-founders.
This documentation is organized in the following sections:
- Overview
- FedML MLOps - Landing FedML into Reality
- FedML Parrot - Simulating the Real World
- FedML Octopus - Simple Connector for Data Silos
- FedML BeeHive - Collaborative Learning on Smartphones/IoTs
- FedML Cheetah - Speedy Training of Large Models
- FedML Benchmarks Benchmarks for FedNLP, FedCV, FedGraphNN and FedIoT
- Resources
FedML is hiring researchers, engineers, product managers, and related interns. If you are interested, Please apply at https://fedml.ai/careers