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[2512.22623] Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback
Summary
This paper presents novel frameworks for communication compression in distributed learning, addressing bandwidth constraints in federated learning by eliminating client-specific control variates while ensuring convergence.
Why It Matters
As distributed learning grows, efficient communication methods become critical. This research offers solutions to reduce communication costs without compromising user privacy, making it highly relevant for developers and researchers in machine learning and federated learning.
Key Takeaways
- Introduces Compressed Aggregate Feedback (CAFe) for efficient communication in federated learning.
- CAFe-S enhances CAFe by incorporating server-guided updates, improving prediction accuracy.
- Proves theoretical advantages of CAFe over existing methods in non-convex scenarios.
- Experimental results validate the proposed frameworks' effectiveness in real-world applications.
- Addresses critical privacy concerns by eliminating client-specific control variates.
Computer Science > Machine Learning arXiv:2512.22623 (cs) [Submitted on 27 Dec 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback Authors:Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li, Hamid Jafarkhani View a PDF of the paper titled Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback, by Tomas Ortega and 2 other authors View PDF Abstract:Distributed learning, particularly Federated Learning (FL), faces a significant bottleneck in the communication cost, particularly the uplink transmission of client-to-server updates, which is often constrained by asymmetric bandwidth limits at the edge. Biased compression techniques are effective in practice, but require error feedback mechanisms to provide theoretical guarantees and to ensure convergence when compression is aggressive. Standard error feedback, however, relies on client-specific control variates, which violates user privacy and is incompatible with stateless clients common in large-scale FL. This paper proposes two novel frameworks that enable biased compression without client-side state or control variates. The first, Compressed Aggregate Feedback (CAFe), uses the globally aggregated update from the previous round as a shared control variate for all clients. The second, Server-Guided Compressed Aggregate Feedback (CAFe-S), extends this idea to scenarios where the server possesses a ...