# youngfish42/Awesome-FL

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

Repository: https://github.com/youngfish42/Awesome-FL
Canonical: https://ross.abutalabs.com/products/awesome-fl
Homepage: https://youngfish42.github.io/Awesome-FL
Language: Python
License: CC-BY-SA-4.0
License Family: other
Topics: awesome, deep-learning, federated-learning, graph-neural-networks, machine-learning, tabular-data, federated-learning-framework, computer-vision, knowledge-graph, paper, security, natural-language-processing, information-retrieval, data-mining, database, system, artificial-intelligence, efficiency, privacy, graph
Last push: 2026-05-20T12:48:40+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 83, release rhythm 49, longevity 100
- inputs: {"age_days": 1566, "days_push": 105, "days_rel": 124, "gap_med": 288.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2013, forks 223 (observed 2026-08-28T04:06:05.250787+00:00)

## What it is
A curated awesome-list of federated learning resources including papers by venue and topic, frameworks, datasets, surveys, tutorials, and workshops. It is automatically tracked for new papers but now updated monthly or quarterly after the maintainer's PhD completion.

## Use cases
- find federated learning papers by conference or research area
- discover federated learning frameworks and datasets
- find surveys and tutorials on federated learning
- track federated learning workshops and journal special issues
- research federated learning on graph data or tabular data
- get started learning federated learning

## When to choose
- you need a comprehensive, categorized bibliography of federated learning research
- you want curated lists of FL frameworks, datasets, and tutorials in one place
- you are surveying FL literature across CV, NLP, security, and systems venues

## When to avoid
- you need actively maintained metadata like author institutions and code links
- you need a runnable federated learning framework rather than a resource list
- you require daily paper updates

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, developer-tools
- domain: machine-learning, artificial-intelligence, privacy, security, tutorials
- platform: -
- tags: awesome-list, federated-learning, papers, datasets, surveys, academic-resources, web-server

## Member repositories
- youngfish42/Awesome-FL (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:05.250787+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 2026-08-30T03:00:55.796800+00:00, confidence not recorded.
  - readme: https://github.com/youngfish42/Awesome-FL (fetched 2026-08-28T04:06:05.250787+00:00, sha 97202777bad5)
  - homepage: https://youngfish42.github.io/Awesome-FL (fetched 2026-08-29T10:40:55.193737+00:00, sha 9e9cf51ca992)
- Data as of 2026-08-30T08:39:29.467469+00:00.