This repository serves as a comprehensive index and resource hub for Federated Learning (FL), targeting researchers, engineers, and practitioners interested in distributed machine learning with a focus on data privacy and locality. It curates a vast collection of academic papers, benchmarks, systems, and applications, providing a structured overview of the FL landscape.

How It Works

The project doesn't implement a specific FL system but rather acts as a meta-resource. It categorizes and links to a wide array of research papers covering core FL concepts, algorithms for model aggregation, convergence analysis, handling statistical heterogeneity, adaptive aggregation, security threats (attacks and defenses), communication efficiency techniques, personalization strategies, decentralized FL, vertical FL, and applications in wireless communication, cloud computing, and deep learning domains.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable software package. No installation or specific requirements are applicable. Links to external resources like benchmarks (e.g., LEAF) and research papers are provided within the README.

Highlighted Details

• Extensive categorization of over 150 research papers spanning 14 application and technical domains within Federated Learning.
• Includes links to key benchmarks like LEAF and systems like FedML, FATE, and FedLearner.
• Covers critical aspects such as security, privacy, communication efficiency, and personalization.
• Provides a broad overview of industry adoption and academic research trends.

Maintenance & Community

The repository is maintained by lokinko. It serves as a community-driven index, with numerous academic papers and industry frameworks referenced, indicating a broad community interest and ongoing research in Federated Learning.

Licensing & Compatibility

The repository itself is a collection of links and information, not software. The licensing of linked projects or papers would vary and is not specified here. Compatibility is not applicable as it's an informational resource.

Limitations & Caveats

This repository is an index and does not provide runnable code or a unified framework. Users must consult the linked resources for specific implementations, benchmarks, or system details. The sheer volume of information may require significant effort to navigate and synthesize.