awesome-federated-learning
Curated federated learning resources including papers, blogs, videos, and projects
GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
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Decision brief
awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
Good fit when
- Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL
- Choose this resource when looking to understand communication-efficient methods specific to deep networks
Avoid when
- Avoid if your project does not require federated learning-specific optimizations or frameworks
- Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (321d since push)
- As of Oct 4, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 4, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/weimingwill/awesome-federated-learningSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A collection of materials for federated learning with a focus on communication efficiency and privacy preservation
Capability facts
- Languages
- shell
Source: github.language · Oct 4, 2026
Categories
Tags
README
Awesome Federated Learning A curated list of materials for federated learning, including blogs, surveys, research papers, and projects. You are very welcome to star it and create a pull request to update it. Federated learning (FL) is attracting considerable attention these years...
For agents
This page has a .md twin and JSON over the API.