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FedML

FedML-AI/FedML

Unified and scalable ML library for distributed training, model serving, federated learning

GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026

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4.1k stars765 forksLast push Oct 28, 2025 Python Apache-2.0

Decision brief

FedML supports comprehensive AI workflows including generative AI services through integration with TensorOpera AI. It excels in large-scale distributed training and federated learning across multiple environments.

Good fit when

  • When you need to run ML tasks on decentralized GPUs or multi-clouds without complex setup
  • For projects requiring scalable model deployment and serving with low latency

Avoid when

  • Avoid if you require a standalone solution that does not integrate with TensorOpera AI services
  • Not recommended for small-scale projects where resource flexibility is less critical

Observed Jul 17, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Slowing (339d since push)
As of Oct 3, 2026
Provenance
Not a fork · Organization account
As of Oct 3, 2026
Security (OSV)
88 low (88 low)
As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install FedML
PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

FEDML is a universal machine learning library supporting large-scale distributed training, model deployment, and federated learning. It integrates TensorOpera AI for comprehensive generative AI services.

Capability facts

Languages
python

Source: github.language · Oct 3, 2026

Categories

Tags

README

FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai) <div align="center" <img src="docs/images/TensorOpera arch.png" wid...

For agents

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