--- title: "Awesome-Federated-Learning vs ml-surveys" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-eugeneyan-ml-surveys" tools: ["chaoyanghe-awesome-federated-learning", "eugeneyan-ml-surveys"] --- # Awesome-Federated-Learning vs ml-surveys *GraphCanon updated Aug 22, 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 ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. [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. [ml-surveys](https://github.com/eugeneyan/ml-surveys) has 2.9k stars, 292 forks, and 2 open issues, last pushed Mar 17, 2023. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys). | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | | --- | --- | --- | | Tagline | FedML - The Research and Production Integrated Federated Learning Library | Survey papers summarizing advances in various AI domains | | Stars | 2,017 | 2,902 | | Forks | 332 | 292 | | Open issues | 3 | 2 | | Language | - | - | | Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability, Model Training | Computer Vision, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | | --- | --- | --- | | Days since push | 1430d | 1254d | | Open issues (now) | 3 | 2 | | Stars delta | Unknown | 0 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/eugeneyan-ml-surveys/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: ml-surveys - **Adopt for:** ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. ## Choose when ### Choose Awesome-Federated-Learning if… - Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, continual-learning. - When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches. ### Choose ml-surveys if… - Tags unique to ml-surveys: deep-learning, embeddings, machine-learning, nlp. - Also covers Computer Vision. - When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning ## 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 ml-surveys - If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary - In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches ## Common questions ### What is the difference between Awesome-Federated-Learning and ml-surveys? Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. ml-surveys: Survey papers summarizing advances in various AI domains. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Federated-Learning over ml-surveys? Choose Awesome-Federated-Learning over ml-surveys when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, continual-learning; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches. ### When should I choose ml-surveys over Awesome-Federated-Learning? Choose ml-surveys over Awesome-Federated-Learning when Tags unique to ml-surveys: deep-learning, embeddings, machine-learning, nlp; Also covers Computer Vision; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning. ### 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 ml-surveys? If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches ### Is Awesome-Federated-Learning or ml-surveys more popular on GitHub? ml-surveys has more GitHub stars (2,902 vs 2,017). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Federated-Learning and ml-surveys open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Federated-Learning or ml-surveys? GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [ml-surveys alternatives](/tools/eugeneyan-ml-surveys/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/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-eugeneyan-ml-surveys.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 ml-surveys? Awesome-Federated-Learning: Dormant. ml-surveys: 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 ml-surveys? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [ml-surveys trust report](/tools/eugeneyan-ml-surveys/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/_