FLsystem-paper
A curated list of FL system-related academic papers and frameworks
GraphCanon updated 2w · GitHub synced 2w
Decision brief
FLsystem-paper is a curated list of federated learning systems literature geared towards providing research and development insights exclusively from big tech companies and open-source projects.
Good fit when
- When you need to focus on federated learning systems contributions from major technology firms like Apple, Google, Meta, Microsoft, IBM, Nvidia, WeBank, and Alibaba.
- To gain insights into real-world device traces and applications of federated learning across different systems.
Avoid when
- If your research scope is broader than federated learning systems; this repository focuses specifically on the system aspects within FL.
- For a comprehensive collection that includes other ML domains, as FLsystem-paper restricts its curation to federated learning systems and closely related works.
- Hosting:
- self hosted - (no information available)
- Pricing:
- freemium - The repository itself is free and open source, but usage might involve proprietary frameworks or projects from big tech companies that could have their own licensing models.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (909d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
git clone https://github.com/AmberLJC/FLsystem-paperSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides an organized collection of federated learning systems literature for research purposes, including papers from big tech companies and open-source projects
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
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README
Awesome Federated Computation Systems Papers
A curated list of FL system-related academic papers, articles, tutorials, slides and projects. Star this repository, and then you can keep abreast of the latest developments of this booming research field.
Papers with 🎓 have been peer-reviewed and presented in academic conferences.
Table of Contents
- Awesome Federated Computation Systems Papers
- Table of Contents
- FL Systems from big tech companies
- Paper
- Framework
- Vertical FL
- Open-source FL Framework
- Edge / Mobile
- Federated Computation Systems
- Optimization for FL Systems
- Security and Privacy
- Real-world FL Application
- Real-world device traces
- Survey
- General insight for FL
- Other FL paper list
FL Systems from big tech companies
Paper
Cross-device
- Apple: Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications |
PDF,PDF - Google: Towards Federated Learning at Scale: System Design |
MLSys21,Github🎓 - Meta: Papaya: Practical, Private, and Scalable Federated Learning |
MLSys22🎓 - Microsoft: FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations |
PDF,Github - Alibaba-1: FederatedScope: A Flexible Federated Learning Platform for Heterogeneity|
PDF - Alibaba-2: FederatedScope: FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning |
KDD22🎓
Federated Analytics
- LinkedIn: LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale |
PDF - Alibaba-3: Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning |
PDF,Github🎓
Cross-silo
- IBM: IBM Federated Learning: An Enterprise Framework White Paper |
PDF,Github - Nvidia: Federated Learning for Healthcare Using NVIDIA Clara |
PDF,Github - WeBank: Federated Learning White Paper V1.0 |
PDF,FATE,KubeFATE, FATE-FLOW, FATE-LLM
Framework
- Cisco: Flame |
Github - OpenMined: PySyft |
Github - Baidu: Paddle |
Github - ByteDance: Fedlearner |
Github - Meta: FLSim | [
Github](https://github.com/facebookresearch/FL
For agents
This page has a .md twin and JSON over the API.