Federated Learning Survey: Aggregation Techniques, Experiments, and Future Directions
Published:Nov 27, 2025 16:50
•1 min read
•ArXiv
Analysis
This ArXiv paper provides a comprehensive overview of federated learning, a crucial area for privacy-preserving machine learning. The survey's focus on aggregation techniques and experimental insights is especially valuable for researchers and practitioners.
Key Takeaways
- •Provides a multi-level taxonomy of aggregation techniques.
- •Offers experimental insights into various federated learning approaches.
- •Highlights future frontiers and research directions in the field.
Reference
“The survey covers a multi-level taxonomy of aggregation techniques.”