# innovation-cat/Awesome-Federated-Machine-Learning

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

Repository: https://github.com/innovation-cat/Awesome-Federated-Machine-Learning
Canonical: https://ross.abutalabs.com/products/awesome-federated-machine-learning
License Family: other
Topics: federated-learning, machine-learning, deep-learning, privacy-preserving-machine-learning, computer-vision, distributed-computing, edge-computing, security, differential-privacy
Last push: 2024-05-30T21:21:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2510, "days_push": 825, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2091, forks 282 (observed 2026-08-28T04:06:12.423564+00:00)

## What it is
A curated awesome-list of federated learning resources including research papers, books, code, tutorials, and videos. It tracks FL advancements across top ML, CV, and data mining conferences.

## Use cases
- find federated learning research papers
- learn about privacy-preserving machine learning
- find open-source federated learning frameworks
- get tutorials and videos on federated learning
- track FL papers from NeurIPS ICML ICLR
- research differential privacy in distributed training

## When to choose
- you need a starting point to survey the federated learning field
- you want curated links to papers, books, and tutorials on FL
- you are researching privacy-preserving distributed ML

## When to avoid
- you need a runnable federated learning framework rather than a resource list
- you need maintained software with a license and releases

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation, developer-tools
- domain: machine-learning, privacy, tutorials, awesome-lists, microservices
- platform: cross-platform
- tags: awesome-list, federated-learning, privacy-preserving-ml, research-papers, curated-list

## Member repositories
- innovation-cat/Awesome-Federated-Machine-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:12.423564+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:55:33.028570+00:00, confidence not recorded.
  - readme: https://github.com/innovation-cat/Awesome-Federated-Machine-Learning (fetched 2026-08-28T04:06:12.423564+00:00, sha 3f196d758a38)
- Data as of 2026-08-30T08:39:29.467469+00:00.