--- title: "pytorch-lightning vs awesome-federated-learning" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightning-ai-pytorch-lightning-vs-weimingwill-awesome-federated-learning" tools: ["lightning-ai-pytorch-lightning", "weimingwill-awesome-federated-learning"] --- # pytorch-lightning vs awesome-federated-learning *GraphCanon updated Jul 11, 2026* ## Verdict Pick pytorch-lightning when pytorch-lightning is primarily Python; awesome-federated-learning is Shell; pick awesome-federated-learning when awesome-federated-learning is primarily Shell; pytorch-lightning is Python. [pytorch-lightning](https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) reports 31k GitHub stars, 3.8k forks, and 1.0k open issues, last pushed Jul 10, 2026. [awesome-federated-learning](https://github.com/EasyFL-AI/EasyFL) has 735 stars, 98 forks, and 0 open issues, last pushed Nov 16, 2025. Figures are from public GitHub metadata via [pytorch-lightning's repository](https://github.com/Lightning-AI/pytorch-lightning) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning). | | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) | | --- | --- | --- | | Tagline | Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes. | All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc. | | Stars | 31,233 | 735 | | Forks | 3,756 | 98 | | Open issues | 1,049 | 0 | | Language | Python | Shell | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training, Computer Vision | Model Training, Vector Databases, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 237d | | Open issues (now) | 1.0k | 0 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/lightning-ai-pytorch-lightning/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) | ## Choose when ### Choose pytorch-lightning if… - pytorch-lightning is primarily Python; awesome-federated-learning is Shell. - License: pytorch-lightning is Apache-2.0, awesome-federated-learning is MIT. - Tags unique to pytorch-lightning: data-science, deep-learning, ai, artificial-intelligence. ### Choose awesome-federated-learning if… - awesome-federated-learning is primarily Shell; pytorch-lightning is Python. - License: awesome-federated-learning is MIT, pytorch-lightning is Apache-2.0. - Tags unique to awesome-federated-learning: federated-learning-framework, data-privacy, communication-efficiency, federated-learning. - Also covers Vector Databases. ## When NOT to use pytorch-lightning - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use awesome-federated-learning - Last GitHub push was 237 days ago (slowing maintenance, Nov 16, 2025). Validate activity before betting a new project on awesome-federated-learning. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between pytorch-lightning and awesome-federated-learning? pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.. awesome-federated-learning: All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.. See the comparison table for live GitHub stats and shared categories. ### When should I choose pytorch-lightning over awesome-federated-learning? Choose pytorch-lightning over awesome-federated-learning when pytorch-lightning is primarily Python; awesome-federated-learning is Shell; License: pytorch-lightning is Apache-2.0, awesome-federated-learning is MIT; Tags unique to pytorch-lightning: data-science, deep-learning, ai, artificial-intelligence. ### When should I choose awesome-federated-learning over pytorch-lightning? Choose awesome-federated-learning over pytorch-lightning when awesome-federated-learning is primarily Shell; pytorch-lightning is Python; License: awesome-federated-learning is MIT, pytorch-lightning is Apache-2.0; Tags unique to awesome-federated-learning: federated-learning-framework, data-privacy, communication-efficiency, federated-learning; Also covers Vector Databases. ### When should I avoid pytorch-lightning? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid awesome-federated-learning? Last GitHub push was 237 days ago (slowing maintenance, Nov 16, 2025). Validate activity before betting a new project on awesome-federated-learning. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is pytorch-lightning or awesome-federated-learning more popular on GitHub? pytorch-lightning has more GitHub stars (31,233 vs 735). Stars measure visibility, not whether either tool fits your constraints. ### Are pytorch-lightning and awesome-federated-learning open source? Yes - both are open-source projects on GitHub (pytorch-lightning: Apache-2.0, awesome-federated-learning: MIT). ### Where can I find alternatives to pytorch-lightning or awesome-federated-learning? GraphCanon lists graph-backed alternatives at [pytorch-lightning alternatives](/tools/lightning-ai-pytorch-lightning/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([pytorch-lightning markdown twin](/tools/lightning-ai-pytorch-lightning/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/lightning-ai-pytorch-lightning-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, pytorch-lightning or awesome-federated-learning? pytorch-lightning: Very active. 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 pytorch-lightning and awesome-federated-learning? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pytorch-lightning trust report](/tools/lightning-ai-pytorch-lightning/trust); [awesome-federated-learning trust report](/tools/weimingwill-awesome-federated-learning/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lightning-ai-pytorch-lightning`](/api/graphcanon/graph?tool=lightning-ai-pytorch-lightning) - 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/_