This repository serves as a comprehensive and up-to-date academic resource hub for Federated Learning (FL). It curates a vast collection of papers, frameworks, datasets, tutorials, and workshop information, catering to researchers, engineers, and practitioners in the field. The primary benefit is providing a centralized, organized, and easily navigable repository for staying abreast of the rapidly evolving FL landscape.

How It Works

The repository functions as a curated index, meticulously categorizing and linking to academic resources related to Federated Learning. It leverages DBLP and other academic search engines to track publications across various top-tier AI, ML, DM, Security, CV, NLP, Database, and Systems conferences and journals. The organization by category and venue allows users to efficiently find relevant literature.

Quick Start & Requirements

This is a curated list of resources, not a software package. No installation or execution is required. The repository is accessible via its GitHub URL.

Highlighted Details

• Comprehensive Coverage: Indexes papers from major conferences (NeurIPS, ICML, ICLR, CVPR, ACL, KDD, etc.) and journals (JMLR, TPAMI, Nature, Science).
• Categorization: Organizes resources by sub-fields of FL (e.g., FL on Graph Data, FL on Tabular Data) and by venue.
• Update Tracker: Utilizes a separate project (FL-paper-update-tracker) for automated tracking of new FL papers.
• Community Driven: Encourages contributions via issues, pull requests, or direct email to keep the repository current.

Maintenance & Community

The repository is maintained by @youngfish42. A notice dated 2024/09/30 indicates that updates to the paper list will become less frequent (monthly/quarterly) and less detailed due to the owner's shift in research focus post-doctoral studies. The repository actively invites more contributors to maintain its value. A QQ group (833638275) is available for communication within the FL community.

Licensing & Compatibility

The repository itself, as a collection of links and metadata, is not subject to typical software licensing. However, the linked resources are governed by their respective publishers' licenses. Compatibility for commercial use or closed-source linking depends entirely on the licenses of the individual papers, frameworks, or datasets referenced.

Limitations & Caveats

The repository's value is directly tied to the diligence of its contributors; the reduced update frequency and detail mentioned in the 2024/09/30 notice may impact its timeliness and comprehensiveness. It does not host any code or datasets directly, only links to external resources.