FedML
FedML is an open-source federated learning framework designed to enable collaborative machine learning across decentralized devices or data silos while preserving data privacy. It provides a unified platform for developing, deploying, and managing federated learning applications, supporting various algorithms, communication protocols, and hardware environments. The framework aims to simplify the implementation of federated learning by offering tools for simulation, distributed training, and real-world deployment.
Developers should learn FedML when working on privacy-preserving machine learning projects, such as in healthcare, finance, or IoT, where data cannot be centralized due to regulatory or security constraints. It is particularly useful for building applications that require training models on distributed data sources, like mobile devices or edge servers, without sharing raw data. FedML helps reduce development time by providing pre-built components and supports scalability across diverse environments.