--- title: "caffe vs FATE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bvlc-caffe-vs-federatedai-fate" tools: ["bvlc-caffe", "federatedai-fate"] --- # caffe vs FATE *GraphCanon updated Aug 4, 2026* ## Verdict Pick caffe if caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency; pick FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. [caffe](http://caffe.berkeleyvision.org/) reports 35k GitHub stars, 18k forks, and 1.5k open issues, last pushed Jul 31, 2024. [FATE](https://github.com/FederatedAI/FATE) has 6.1k stars, 1.6k forks, and 21 open issues, last pushed Nov 19, 2024. Figures are from public GitHub metadata via [caffe's repository](https://github.com/BVLC/caffe) and [FATE's repository](https://github.com/FederatedAI/FATE). | | [caffe](/tools/bvlc-caffe.md) | [FATE](/tools/federatedai-fate.md) | | --- | --- | --- | | Tagline | Caffe is a fast open framework for deep learning. | An Industrial Grade Federated Learning Framework | | Stars | 34,573 | 6,089 | | Forks | 18,443 | 1,568 | | Open issues | 1,471 | 21 | | Language | C++ | Python | | Adopt for | Caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. | FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. | | Persona | - | - | | Runtime | - | - | | License | Caffe is available under the BSD 2-Clause license. | Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users. | | Categories | Computer Vision, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caffe](/tools/bvlc-caffe.md) | [FATE](/tools/federatedai-fate.md) | | --- | --- | --- | | Days since push | 732d | 623d | | Open issues (now) | 1.5k | 21 | | Full report | [trust report](/tools/bvlc-caffe/trust.md) | [trust report](/tools/federatedai-fate/trust.md) | ## Decision facts: caffe - **Pricing:** freemium - Free to use under open source licensing with no monetary charges. - **Adopt for:** Caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. - **License detail:** Caffe is available under the BSD 2-Clause license. ## 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. ## Choose when ### Choose caffe if… - caffe is primarily C++; FATE is Python. - License: caffe is Other, FATE is Apache-2.0. - Pricing: Free to use under open source licensing with no monetary charges.. - Tags unique to caffe: deep-learning, vision. - Also covers Computer Vision. - - You need a framework that supports high-performance convolutional networks particularly suited for image classification ### Choose FATE if… - FATE is primarily Python; caffe is C++. - License: FATE is Apache-2.0, caffe is Other. - Tags unique to FATE: algorithm, fate, federated-learning, privacy-preserving. - When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information ## When NOT to use caffe - - Your primary task involves natural language processing rather than computer vision challenges, where specialized frameworks might outperform Caffe - - You seek a framework that integrates seamlessly with Python for both training and inference, as Caffe relies heavily on C++ for its core operations ## 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 ## Common questions ### What is the difference between caffe and FATE? caffe: Caffe is a fast open framework for deep learning.. FATE: An Industrial Grade Federated Learning Framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose caffe over FATE? Choose caffe over FATE when caffe is primarily C++; FATE is Python; License: caffe is Other, FATE is Apache-2.0; Pricing: Free to use under open source licensing with no monetary charges.; Tags unique to caffe: deep-learning, vision; Also covers Computer Vision; - You need a framework that supports high-performance convolutional networks particularly suited for image classification. ### When should I choose FATE over caffe? Choose FATE over caffe when FATE is primarily Python; caffe is C++; License: FATE is Apache-2.0, caffe is Other; Tags unique to FATE: algorithm, fate, federated-learning, privacy-preserving; When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information. ### When should I avoid caffe? - Your primary task involves natural language processing rather than computer vision challenges, where specialized frameworks might outperform Caffe - You seek a framework that integrates seamlessly with Python for both training and inference, as Caffe relies heavily on C++ for its core operations ### 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 ### Is caffe or FATE more popular on GitHub? caffe has more GitHub stars (34,573 vs 6,089). Stars measure visibility, not whether either tool fits your constraints. ### Are caffe and FATE open source? Yes - both are open-source projects on GitHub (caffe: Other, FATE: Apache-2.0). ### Where can I find alternatives to caffe or FATE? GraphCanon lists graph-backed alternatives at [caffe alternatives](/tools/bvlc-caffe/alternatives) and [FATE alternatives](/tools/federatedai-fate/alternatives) ([caffe markdown twin](/tools/bvlc-caffe/alternatives.md), [FATE markdown twin](/tools/federatedai-fate/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/bvlc-caffe-vs-federatedai-fate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caffe or FATE? caffe: Dormant. FATE: 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 caffe and FATE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caffe trust report](/tools/bvlc-caffe/trust); [FATE trust report](/tools/federatedai-fate/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bvlc-caffe`](/api/graphcanon/graph?tool=bvlc-caffe) - 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/_