# lokinko/Federated-Learning

联邦学习

Repository: https://github.com/lokinko/Federated-Learning
Canonical: https://ross.abutalabs.com/products/lokinko-federated-learning
License Family: other
Last push: 2023-03-14T14:15:24+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2142, "days_push": 1268, "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 1149, forks 201 (observed 2026-08-28T04:03:46.412080+00:00)

## What it is
A curated collection of federated learning resources, including introductory materials, survey papers, and research articles on privacy-preserving distributed machine learning. It serves primarily as a reading list rather than runnable software.

## Use cases
- learn federated learning from scratch
- find survey papers on federated learning
- research privacy-preserving distributed machine learning
- find papers on federated learning for IoT and edge computing
- get started with decentralized model training
- study federated learning security threats and personalization techniques

## When to choose
- you want a curated reading list of federated learning papers and tutorials
- you are researching the state of the art in privacy-preserving distributed ML
- you need references for a paper or course on federated learning

## When to avoid
- you need a working federated learning framework to run experiments
- you want production-ready code with active maintenance
- you need a library with APIs rather than a paper collection

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, privacy, tutorials, awesome-lists
- platform: cross-platform
- tags: federated-learning, paper-collection, privacy-preserving-ml, curated-list, distributed-training

## Member repositories
- lokinko/Federated-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:46.412080+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 2026-08-30T06:33:45.843947+00:00, confidence not recorded.
  - readme: https://github.com/lokinko/Federated-Learning (fetched 2026-08-28T04:03:46.412080+00:00, sha 854add75a70f)
- Data as of 2026-08-30T08:39:29.467469+00:00.