Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library
GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
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Decision brief
FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
Good fit when
- When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.
- For projects involving wireless communication systems where computational and communication efficiencies are critical factors.
Avoid when
- If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity.
- When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1491d 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/chaoyanghe/Awesome-Federated-LearningSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Federated learning library supporting research and production with various features including adversarial attacks, privacy, hierarchical models, decentralized approaches, computation efficiency, and more.
Capability facts
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Categories
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README
System Challenges: communication and computational resource constrained, software and hardware heterogeneity, and FL wireless communication system
For agents
This page has a .md twin and JSON over the API.