--- title: "FATE vs awesome-federated-learning" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/federatedai-fate-vs-weimingwill-awesome-federated-learning" tools: ["federatedai-fate", "weimingwill-awesome-federated-learning"] --- # FATE vs awesome-federated-learning *GraphCanon updated Aug 4, 2026* ## Verdict Pick FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes; pick awesome-federated-learning if awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. [FATE](https://github.com/FederatedAI/FATE) reports 6.1k GitHub stars, 1.6k forks, and 21 open issues, last pushed Nov 19, 2024. [awesome-federated-learning](https://github.com/EasyFL-AI/EasyFL) has 738 stars, 98 forks, and 0 open issues, last pushed Nov 16, 2025. Figures are from public GitHub metadata via [FATE's repository](https://github.com/FederatedAI/FATE) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning). | | [FATE](/tools/federatedai-fate.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) | | --- | --- | --- | | Tagline | An Industrial Grade Federated Learning Framework | Curated federated learning resources including papers, blogs, videos, and projects | | Stars | 6,089 | 738 | | Forks | 1,568 | 98 | | Open issues | 21 | 0 | | Language | Python | Shell | | Adopt for | FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. | awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users. | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FATE](/tools/federatedai-fate.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 623d | 261d | | Open issues (now) | 21 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/federatedai-fate/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) | ## 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. ## Decision facts: awesome-federated-learning - **Adopt for:** awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. ## Choose when ### Choose FATE if… - FATE is primarily Python; awesome-federated-learning is Shell. - License: FATE is Apache-2.0, awesome-federated-learning is MIT. - Tags unique to FATE: algorithm, fate, privacy-preserving. - When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information ### Choose awesome-federated-learning if… - awesome-federated-learning is primarily Shell; FATE is Python. - License: awesome-federated-learning is MIT, FATE is Apache-2.0. - Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, non-iid, statistical-heterogeneity. - Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL ## 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 ## When NOT to use awesome-federated-learning - Avoid if your project does not require federated learning-specific optimizations or frameworks - Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL ## Common questions ### What is the difference between FATE and awesome-federated-learning? FATE: An Industrial Grade Federated Learning Framework. awesome-federated-learning: Curated federated learning resources including papers, blogs, videos, and projects. See the comparison table for live GitHub stats and shared categories. ### When should I choose FATE over awesome-federated-learning? Choose FATE over awesome-federated-learning when FATE is primarily Python; awesome-federated-learning is Shell; License: FATE is Apache-2.0, awesome-federated-learning is MIT; Tags unique to FATE: algorithm, fate, privacy-preserving; When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information. ### When should I choose awesome-federated-learning over FATE? Choose awesome-federated-learning over FATE when awesome-federated-learning is primarily Shell; FATE is Python; License: awesome-federated-learning is MIT, FATE is Apache-2.0; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, non-iid, statistical-heterogeneity; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL. ### 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 ### When should I avoid awesome-federated-learning? Avoid if your project does not require federated learning-specific optimizations or frameworks Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL ### Is FATE or awesome-federated-learning more popular on GitHub? FATE has more GitHub stars (6,089 vs 738). Stars measure visibility, not whether either tool fits your constraints. ### Are FATE and awesome-federated-learning open source? Yes - both are open-source projects on GitHub (FATE: Apache-2.0, awesome-federated-learning: MIT). ### Where can I find alternatives to FATE or awesome-federated-learning? GraphCanon lists graph-backed alternatives at [FATE alternatives](/tools/federatedai-fate/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([FATE markdown twin](/tools/federatedai-fate/alternatives.md), [awesome-federated-learning markdown twin](/tools/weimingwill-awesome-federated-learning/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/federatedai-fate-vs-weimingwill-awesome-federated-learning.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FATE or awesome-federated-learning? FATE: Dormant. awesome-federated-learning: Slowing. 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 FATE and awesome-federated-learning? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FATE trust report](/tools/federatedai-fate/trust); [awesome-federated-learning trust report](/tools/weimingwill-awesome-federated-learning/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=federatedai-fate`](/api/graphcanon/graph?tool=federatedai-fate) - 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/_