# chaoyanghe/Awesome-Federated-Learning

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Repository: https://github.com/chaoyanghe/Awesome-Federated-Learning
Canonical: https://ross.abutalabs.com/products/awesome-federated-learning
License Family: other
Topics: federated-learning, machine-learning, distributed-optimization, non-iid, vertical-federated-learning, decentralized-federated-learning, hierarchical-federated-learning, neural-architecture-search, transfer-learning, continual-learning, semi-supervised-learning, adversarial-attack-and-defense, privacy, communication-efficiency, straggler-problem, computation-efficiency, wireless-communication, interpretability, incentive-mechanism, computer-vision
Last push: 2022-09-03T20:03:02+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": 2252, "days_push": 1460, "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 2016, forks 332 (observed 2026-08-28T04:06:05.572792+00:00)

## What it is
A curated awesome-list of federated learning research publications organized by venue, problem, and method, maintained as part of the FedML project. It catalogs papers on topics like non-IID data, communication efficiency, privacy, and personalization.

## Use cases
- find federated learning papers on non-IID data
- survey recent federated learning research
- learn about communication-efficient distributed training
- research privacy-preserving machine learning
- find papers on personalized federated learning
- prepare a literature review on distributed optimization

## When to choose
- you need a curated reading list of federated learning publications
- you are surveying state-of-the-art FL methods by problem area
- you want pointers to top-tier conference papers on distributed ML

## When to avoid
- you need a runnable federated learning framework rather than a paper list
- you need actively updated content - the list moved to the FedML repository
- you need production federated learning tooling

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

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:05.572792+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 2026-08-30T03:00:42.072653+00:00, confidence not recorded.
  - readme: https://github.com/chaoyanghe/Awesome-Federated-Learning (fetched 2026-08-28T04:06:05.572792+00:00, sha 0aadaba06005)
- Data as of 2026-08-30T08:39:29.467469+00:00.