---
title: "Awesome-Federated-Learning vs FedML"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-fedml-ai-fedml"
tools: ["chaoyanghe-awesome-federated-learning", "fedml-ai-fedml"]
---

# Awesome-Federated-Learning vs FedML

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick Awesome-Federated-Learning if fedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency; pick FedML if 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.

[Awesome-Federated-Learning](https://github.com/chaoyanghe/Awesome-Federated-Learning) reports 2.0k GitHub stars, 332 forks, and 3 open issues, last pushed Sep 3, 2022. [FedML](https://TensorOpera.ai) has 4.1k stars, 765 forks, and 148 open issues, last pushed Oct 28, 2025. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [FedML's repository](https://github.com/FedML-AI/FedML).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [FedML](/tools/fedml-ai-fedml.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | Unified and scalable ML library for distributed training, model serving, federated learning |
| Stars | 2,017 | 4,055 |
| Forks | 332 | 765 |
| Open issues | 3 | 148 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | 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. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 |
| Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Inference & Serving, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [FedML](/tools/fedml-ai-fedml.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 1430d | 280d |
| Open issues (now) | 3 | 148 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/fedml-ai-fedml/trust.md) |

## Decision facts: Awesome-Federated-Learning

- **Adopt for:** FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.

## Decision facts: FedML

- **Adopt for:** 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.

## Choose when

### Choose Awesome-Federated-Learning if…

- Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision.
- When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.
- Leaner open-issue backlog (3).

### Choose FedML if…

- Tags unique to FedML: ai-agent, deep-learning, distributed-training, edge-ai.
- Also covers Inference & Serving.
- When you need to run ML tasks on decentralized GPUs or multi-clouds without complex setup

## When NOT to use Awesome-Federated-Learning

- If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity.
- When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.

## When NOT to use FedML

- 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
- If your project strictly demands operations within a single cloud platform without cross-cloud support

## Common questions

### What is the difference between Awesome-Federated-Learning and FedML?

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. FedML: Unified and scalable ML library for distributed training, model serving, federated learning. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-Federated-Learning over FedML?

Choose Awesome-Federated-Learning over FedML when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches; Leaner open-issue backlog (3).

### When should I choose FedML over Awesome-Federated-Learning?

Choose FedML over Awesome-Federated-Learning when Tags unique to FedML: ai-agent, deep-learning, distributed-training, edge-ai; Also covers Inference & Serving; When you need to run ML tasks on decentralized GPUs or multi-clouds without complex setup.

### When should I avoid Awesome-Federated-Learning?

If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity. When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.

### When should I avoid FedML?

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 If your project strictly demands operations within a single cloud platform without cross-cloud support

### Is Awesome-Federated-Learning or FedML more popular on GitHub?

FedML has more GitHub stars (4,055 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and FedML open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Awesome-Federated-Learning or FedML?

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [FedML alternatives](/tools/fedml-ai-fedml/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [FedML markdown twin](/tools/fedml-ai-fedml/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/chaoyanghe-awesome-federated-learning-vs-fedml-ai-fedml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-Federated-Learning or FedML?

Awesome-Federated-Learning: Dormant. FedML: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for Awesome-Federated-Learning and FedML?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [FedML trust report](/tools/fedml-ai-fedml/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning`](/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_