FedML-AI/FedML

Health v2 · maintenance only

Adoption not part of the score

4062 stars · 765 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

FedML (TensorOpera) is a unified Python library for large-scale distributed training, model serving, and federated learning across GPU clouds, on-premise clusters, edge servers, and smartphones. It includes a cross-cloud scheduler (Launch) that pairs AI jobs with economical GPU resources and auto-provisions them.

Use cases

• run federated learning across edge devices and clouds
• train large models on distributed multi-cloud GPUs
• deploy and serve models with low latency
• schedule AI jobs on the cheapest available GPU cloud
• train models on smartphones and IoT devices
• manage on-premise GPU clusters for AI workloads

When to choose

• you need federated or cross-silo learning across heterogeneous devices
• you want one library spanning training, deployment, and multi-cloud scheduling
• you need on-device or edge training for mobile/IoT scenarios

When to avoid

• you only need simple single-machine model training
• you want a lightweight inference-only serving tool
• you prefer a fully managed service without an open-source library

Facets

library · maturity active

machine-learning llm-training llm-inference deployment gpu-computing agent-framework machine-learning deep-learning large-language-models artificial-intelligence microservices gpu-computing developer-tools python cross-platform cloud federated-learning distributed-training mlops edge-ai model-deployment cross-cloud-scheduler on-device-training model-serving docker gpu

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