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

# Awesome-Federated-Learning vs FATE

*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 FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes.

[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. [FATE](https://github.com/FederatedAI/FATE) has 6.1k stars, 1.6k forks, and 21 open issues, last pushed Nov 19, 2024. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [FATE's repository](https://github.com/FederatedAI/FATE).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [FATE](/tools/federatedai-fate.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | An Industrial Grade Federated Learning Framework |
| Stars | 2,017 | 6,089 |
| Forks | 332 | 1,568 |
| Open issues | 3 | 21 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users. |
| Categories | Evaluation & Observability, Model Training | Model Training |

## Trust and health

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

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [FATE](/tools/federatedai-fate.md) |
| --- | --- | --- |
| Days since push | 1430d | 623d |
| Open issues (now) | 3 | 21 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/federatedai-fate/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: FATE

- **Adopt for:** FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes.
- **License detail:** Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users.

## Choose when

### Choose Awesome-Federated-Learning if…

- Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision.
- Also covers Evaluation & Observability.
- When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.

### Choose FATE if…

- Tags unique to FATE: algorithm, fate, machine-learning, privacy-preserving.
- When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information
- More GitHub stars (6.1k vs 2.0k) - visibility, not fit.

## 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 FATE

- In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities
- If your project does not require federated learning's collaborative model training across disjoint data sets

## Common questions

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

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. FATE: An Industrial Grade Federated Learning Framework. See the comparison table for live GitHub stats and shared categories.

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

Choose Awesome-Federated-Learning over FATE when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision; Also covers Evaluation & Observability; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.

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

Choose FATE over Awesome-Federated-Learning when Tags unique to FATE: algorithm, fate, machine-learning, privacy-preserving; When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information; More GitHub stars (6.1k vs 2.0k) - visibility, not fit.

### 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 FATE?

In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities If your project does not require federated learning's collaborative model training across disjoint data sets

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

FATE has more GitHub stars (6,089 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [FATE alternatives](/tools/federatedai-fate/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [FATE markdown twin](/tools/federatedai-fate/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-federatedai-fate.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 FATE?

Awesome-Federated-Learning: Dormant. FATE: Dormant. 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 FATE?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [FATE trust report](/tools/federatedai-fate/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/_