Mapping federated learning's invasion of every top-tier venue

What it does

Awesome-FL is a curated index that catalogs federated learning papers, frameworks, datasets, and tutorials. It organizes research by academic venue rather than technique, covering AI, ML, security, computer vision, NLP, databases, networks, and systems conferences, plus dedicated tracks for graph neural networks and tabular data. The list also links to an automated paper tracker and a companion website for easier browsing.

The interesting bit

The maintainer finished their PhD in September 2024 and announced a step back: updates are now monthly or quarterly, and metadata like author affiliations and open-source code links are no longer actively maintained. The repo has become a snapshot of one researcher’s doctoral obsession, slowly freezing unless new contributors take over.

Key highlights

• Papers sorted by top-tier venue: NeurIPS, ICML, ICLR, KDD, S&P, CCS, SIGCOMM, OSDI, and roughly a dozen others
• Dedicated sections for graph data/GNNs and tabular data (XGBoost, GBDT, etc.)
• Companion auto-tracker project (FL-paper-update-tracker) and a GitHub Pages site for full content
• Includes non-paper resources: frameworks, datasets, surveys, tutorials, and workshop tracks
• Explicit call for contributors to keep the list alive post-PhD

Caveats

• Update frequency and depth have been reduced since September 2024; author institutions and code links are no longer actively maintained
• The README warns that the full content may not display on GitHub and directs users to the external homepage

Verdict

Worth bookmarking if you are doing a literature review or trying to trace how federated learning jumped from ML conferences to security and systems venues. Less useful if you want actively maintained metadata or a hands-on coding tutorial.