FedMPDD: Privacy-Preserving Federated Learning with Communication Efficiency
Published:Dec 23, 2025 22:25
•1 min read
•ArXiv
Analysis
The article introduces FedMPDD, a novel approach for federated learning. This method focuses on communication efficiency while maintaining privacy, a critical concern in distributed machine learning.
Key Takeaways
- •Addresses the challenge of communication costs in federated learning.
- •Prioritizes privacy through the use of Projected Directional Derivative.
- •Contributes to the advancement of privacy-preserving machine learning techniques.
Reference
“FedMPDD leverages Projected Directional Derivative for privacy preservation.”