---
title: "Awesome-Federated-Learning vs RLTF"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-zyq-scut-rltf"
tools: ["chaoyanghe-awesome-federated-learning", "zyq-scut-rltf"]
---

# Awesome-Federated-Learning vs RLTF

*GraphCanon updated Aug 5, 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 RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.

[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. [RLTF](https://github.com/Zyq-scut/RLTF) has 134 stars, 7 forks, and 0 open issues, last pushed Oct 5, 2024. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [RLTF's repository](https://github.com/Zyq-scut/RLTF).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [RLTF](/tools/zyq-scut-rltf.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | Accepted by Transactions on Machine Learning Research (TMLR) |
| Stars | 2,017 | 134 |
| Forks | 332 | 7 |
| Open issues | 3 | 0 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers. |
| Persona | - | - |
| Runtime | - | - |
| License | - | BSD-3-Clause |
| 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) | [RLTF](/tools/zyq-scut-rltf.md) |
| --- | --- | --- |
| Days since push | 1430d | 669d |
| Open issues (now) | 3 | 0 |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/zyq-scut-rltf/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: RLTF

- **Adopt for:** RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.

## 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 RLTF if…

- Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions.
- Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks.
- More recently updated (last pushed Oct 5, 2024).

## 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 RLTF

- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation.
- Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.

## Common questions

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

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. RLTF: Accepted by Transactions on Machine Learning Research (TMLR). See the comparison table for live GitHub stats and shared categories.

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

Choose Awesome-Federated-Learning over RLTF 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 RLTF over Awesome-Federated-Learning?

Choose RLTF over Awesome-Federated-Learning when Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions; Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks; More recently updated (last pushed Oct 5, 2024).

### 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 RLTF?

Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation. Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.

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

Awesome-Federated-Learning has more GitHub stars (2,017 vs 134). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [RLTF alternatives](/tools/zyq-scut-rltf/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [RLTF markdown twin](/tools/zyq-scut-rltf/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-zyq-scut-rltf.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 RLTF?

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

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