Awesome Federated Learning Systems

Overview

A curated list of academic papers on federated learning systems. Maintained by a community contributor, it organizes research for developers exploring privacy-preserving distributed machine learning.

Best for

Best for
Researchers and engineers new to federated learning systems who need a starting point for literature discovery.

Use cases

• Surveying state-of-the-art federated learning system architectures
• Identifying key challenges in communication-efficient distributed training
• Benchmarking papers on client selection and aggregation methods

Notes

A curated list of academic papers on federated learning systems. Maintained by a community contributor, it organizes research for developers exploring privacy-preserving distributed machine learning.

75 stars on GitHub. Last updated 2024-02-07.

Use cases

• Surveying state-of-the-art federated learning system architectures
• Identifying key challenges in communication-efficient distributed training
• Benchmarking papers on client selection and aggregation methods

Pros

• Focused curation saves time on literature review
• Open source and community maintained
• Covers both theoretical and systems papers

Cons

• Low star count suggests limited community adoption
• Only links to papers, no code implementations
• Primarily a reading list, not a deployable tool

Indexed from awesome-llmops and enriched against its public facts.