--- title: "FLsystem-paper vs Awesome-AutoDL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amberljc-flsystem-paper-vs-d-x-y-awesome-autodl" tools: ["amberljc-flsystem-paper", "d-x-y-awesome-autodl"] --- # FLsystem-paper vs Awesome-AutoDL *GraphCanon updated Aug 4, 2026* ## Verdict Pick FLsystem-paper if fLsystem-paper is a curated list of federated learning systems literature geared towards providing research and development insights exclusively from big tech companies and open-source projects; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. [FLsystem-paper](https://github.com/AmberLJC/FLsystem-paper) reports 75 GitHub stars, 7 forks, and 1 open issues, last pushed Feb 7, 2024. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) has 2.3k stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. Figures are from public GitHub metadata via [FLsystem-paper's repository](https://github.com/AmberLJC/FLsystem-paper) and [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL). | | [FLsystem-paper](/tools/amberljc-flsystem-paper.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Tagline | A curated list of FL system-related academic papers and frameworks | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | | Stars | 75 | 2,339 | | Forks | 7 | 319 | | Open issues | 1 | 2 | | Language | - | Python | | Adopt for | FLsystem-paper is a curated list of federated learning systems literature geared towards providing research and development insights exclusively from big tech companies and open-source projects. | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | | Persona | - | - | | Runtime | - | - | | License | (unknown) | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | | Categories | Developer Tools, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FLsystem-paper](/tools/amberljc-flsystem-paper.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Days since push | 909d | 1408d | | Open issues (now) | 1 | 2 | | Full report | [trust report](/tools/amberljc-flsystem-paper/trust.md) | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | ## Decision facts: FLsystem-paper - **Hosting:** self hosted - (no information available) - **Pricing:** freemium - The repository itself is free and open source, but usage might involve proprietary frameworks or projects from big tech companies that could have their own licensing models. - **Adopt for:** FLsystem-paper is a curated list of federated learning systems literature geared towards providing research and development insights exclusively from big tech companies and open-source projects. - **License detail:** (unknown) ## Decision facts: Awesome-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## Choose when ### Choose FLsystem-paper if… - (no information available) - Pricing: The repository itself is free and open source, but usage might involve proprietary frameworks or projects from big tech companies that could have their own licensing models.. - Tags unique to FLsystem-paper: federated-learning, machine-learning, papers. - When you need to focus on federated learning systems contributions from major technology firms like Apple, Google, Meta, Microsoft, IBM, Nvidia, WeBank, and Alibaba. ### Choose Awesome-AutoDL if… - Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). - More GitHub stars (2.3k vs 75) - visibility, not fit. ## When NOT to use FLsystem-paper - If your research scope is broader than federated learning systems; this repository focuses specifically on the system aspects within FL. - For a comprehensive collection that includes other ML domains, as FLsystem-paper restricts its curation to federated learning systems and closely related works. ## When NOT to use Awesome-AutoDL - Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. - Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ## Common questions ### What is the difference between FLsystem-paper and Awesome-AutoDL? FLsystem-paper: A curated list of FL system-related academic papers and frameworks. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories. ### When should I choose FLsystem-paper over Awesome-AutoDL? Choose FLsystem-paper over Awesome-AutoDL when (no information available); Pricing: The repository itself is free and open source, but usage might involve proprietary frameworks or projects from big tech companies that could have their own licensing models.; Tags unique to FLsystem-paper: federated-learning, machine-learning, papers; When you need to focus on federated learning systems contributions from major technology firms like Apple, Google, Meta, Microsoft, IBM, Nvidia, WeBank, and Alibaba. ### When should I choose Awesome-AutoDL over FLsystem-paper? Choose Awesome-AutoDL over FLsystem-paper when Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS); More GitHub stars (2.3k vs 75) - visibility, not fit. ### When should I avoid FLsystem-paper? If your research scope is broader than federated learning systems; this repository focuses specifically on the system aspects within FL. For a comprehensive collection that includes other ML domains, as FLsystem-paper restricts its curation to federated learning systems and closely related works. ### When should I avoid Awesome-AutoDL? Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ### Is FLsystem-paper or Awesome-AutoDL more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 75). Stars measure visibility, not whether either tool fits your constraints. ### Are FLsystem-paper and Awesome-AutoDL open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to FLsystem-paper or Awesome-AutoDL? GraphCanon lists graph-backed alternatives at [FLsystem-paper alternatives](/tools/amberljc-flsystem-paper/alternatives) and [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) ([FLsystem-paper markdown twin](/tools/amberljc-flsystem-paper/alternatives.md), [Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/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/amberljc-flsystem-paper-vs-d-x-y-awesome-autodl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FLsystem-paper or Awesome-AutoDL? FLsystem-paper: Dormant. Awesome-AutoDL: 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 FLsystem-paper and Awesome-AutoDL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FLsystem-paper trust report](/tools/amberljc-flsystem-paper/trust); [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amberljc-flsystem-paper`](/api/graphcanon/graph?tool=amberljc-flsystem-paper) - 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/_