Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback
Research Paper#Distributed Learning, Federated Learning, Communication Compression🔬 Research|Analyzed: Jan 3, 2026 19:50•
Published: Dec 27, 2025 15:29
•1 min read
•ArXivAnalysis
This paper addresses the communication bottleneck in distributed learning, particularly Federated Learning (FL), focusing on the uplink transmission cost. It proposes two novel frameworks, CAFe and CAFe-S, that enable biased compression without client-side state, addressing privacy concerns and stateless client compatibility. The paper provides theoretical guarantees and convergence analysis, demonstrating superiority over existing compression schemes in FL scenarios. The core contribution lies in the innovative use of aggregate and server-guided feedback to improve compression efficiency and convergence.
Key Takeaways
- •Addresses communication bottlenecks in distributed learning, especially in Federated Learning.
- •Proposes CAFe and CAFe-S frameworks for biased compression without client-side state.
- •Provides theoretical guarantees and convergence analysis.
- •Demonstrates superiority over existing compression schemes in FL scenarios.
- •Focuses on improving compression efficiency and convergence through aggregate and server-guided feedback.
Reference / Citation
View Original"The paper proposes two novel frameworks that enable biased compression without client-side state or control variates."