FedML builds simple and versatile APIs for machine learning running anywhere and at any scale. In other words, FedML supports both federated learning for data silos and distributed training for acceleration with MLOps and Open Source support, covering cutting-edge academia research and industrial grade use cases.

  • FedML Cheetah - Accelerate Model Training with User-friendly Distributed Training

  • FedML Parrot - Simulating federated learning in the real world: (1) simulate FL using a single process (2) MPI-based FL Simulator (3) NCCL-based FL Simulator (fastest)

  • FedML Octopus - Cross-silo Federated Learning for cross-organization/account training, including Python-based edge SDK.

  • FedML Beehive - Cross-device Federated Learning for Smartphones and IoTs, including edge SDK for Android/iOS and embedded Linux.

  • FedML MLOps: FedML’s machine learning operation pipeline for AI running anywhere at any scale.

  • Model Serving: we focus on providing a better user experience for edge AI.