--- title: "Awesome-Federated-Learning vs Failed-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-kennethleungty-failed-ml" tools: ["chaoyanghe-awesome-federated-learning", "kennethleungty-failed-ml"] --- # Awesome-Federated-Learning vs Failed-ML *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 Failed-ML if failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning 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. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) has 753 stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML). | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Tagline | FedML - The Research and Production Integrated Federated Learning Library | Compilation of high-profile real-world examples of failed machine learning projects | | Stars | 2,017 | 753 | | Forks | 332 | 51 | | Open issues | 3 | 0 | | Language | - | - | | Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Days since push | 1430d | 777d | | Open issues (now) | 3 | 0 | | Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/kennethleungty-failed-ml/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: Failed-ML - **Hosting:** self hosted - **Pricing:** freemium - Open source under MIT license but no additional paid features are mentioned. - **Requirements:** Not a software tool that requires installation. Informational repository intended for reading and learning. - **Adopt for:** Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. ## Choose when ### Choose Awesome-Federated-Learning if… - Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, continual-learning. - Also covers Model Training. - When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches. ### Choose Failed-ML if… - Pricing: Open source under MIT license but no additional paid features are mentioned.. - Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.. - Tags unique to Failed-ML: ai, artificial-intelligence, classification, data-engineering. - When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ## 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 Failed-ML - If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. - When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ## Common questions ### What is the difference between Awesome-Federated-Learning and Failed-ML? Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Federated-Learning over Failed-ML? Choose Awesome-Federated-Learning over Failed-ML when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, continual-learning; Also covers Model Training; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches. ### When should I choose Failed-ML over Awesome-Federated-Learning? Choose Failed-ML over Awesome-Federated-Learning when Pricing: Open source under MIT license but no additional paid features are mentioned.; Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.; Tags unique to Failed-ML: ai, artificial-intelligence, classification, data-engineering; When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ### 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 Failed-ML? If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ### Is Awesome-Federated-Learning or Failed-ML more popular on GitHub? Awesome-Federated-Learning has more GitHub stars (2,017 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Federated-Learning and Failed-ML open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Federated-Learning or Failed-ML? GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [Failed-ML markdown twin](/tools/kennethleungty-failed-ml/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-kennethleungty-failed-ml.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 Failed-ML? Awesome-Federated-Learning: Dormant. Failed-ML: 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 Failed-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [Failed-ML trust report](/tools/kennethleungty-failed-ml/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/_