Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback

• URL: http://arxiv.org/abs/2512.22623v1
• Date: Sat, 27 Dec 2025 15:29:42 GMT
• Title: Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback
• Authors: Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li, Hamid Jafarkhani,
• Abstract summary: Distributed learning, particularly Federated Learning (FL), faces a significant bottleneck in the communication cost.<n>This paper proposes two novel frameworks that enable biased compression without client-side state or control variables.<n> Experimental results in FL scenarios validate our approaches.
• Score: 43.69787537805927
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 small private dataset; it generates a server-guided candidate update to be used as a more accurate predictor. We consider Distributed Gradient Descent (DGD) as a representative algorithm and analytically prove CAFe's superiority to Distributed Compressed Gradient Descent (DCGD) with biased compression in the non-convex regime with bounded gradient dissimilarity. We further prove that CAFe-S converges to a stationary point, with a rate that improves as the server's data become more representative. Experimental results in FL scenarios validate the superiority of our approaches over existing compression schemes.

Related papers

• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework [57.04850867402913]
  Federated clustering addresses the challenge of extracting patterns from decentralized, unlabeled data.<n>We propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing.<n>Our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10% (NMI) over federated baselines while maintaining provable privacy guarantees.
  arXiv  Detail & Related papers  (2025-11-14T03:05:22Z)
• FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning [7.461859467262201]
  Federated Learning (FL) enables collaborative model training across decentralized clients.<n>FL suffers from significant communication overhead due to the frequent exchange of high-dimensional model updates over constrained networks.<n>We present FedSparQ, a lightweight compression framework that dynamically sparsifies the gradient of each client.
  arXiv  Detail & Related papers  (2025-11-05T12:38:08Z)
• Communication Compression for Distributed Learning without Control Variates [43.69787537805927]
  We propose a novel distributed algorithm that allows compressible clients by exploiting past aggregated updates.<n> Experimental results confirm that CAFe outperforms existing distributed compression learning schemes.
  arXiv  Detail & Related papers  (2024-12-05T18:46:20Z)
• Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification [49.66272783945571]
  Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds. We propose bidirectional communication-efficient FedS based on Entity-Wise Top-K Sparsification strategy.
  arXiv  Detail & Related papers  (2024-06-19T05:26:02Z)
• Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes [54.18186259484828]
  In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds. We show strong evidences that variable-length is beneficial for compression in FL. We present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response to the dynamics of model updates.
  arXiv  Detail & Related papers  (2024-02-06T07:25:21Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning [28.185096784982544]
  Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. We propose a heterogeneous-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3$times$ speedup compared to other methods.
  arXiv  Detail & Related papers  (2022-12-19T14:19:07Z)
• Optimal Rate Adaption in Federated Learning with Compressed Communications [28.16239232265479]
  Federated Learning incurs high communication overhead, which can be greatly alleviated by compression for model updates. tradeoff between compression and model accuracy in the networked environment remains unclear. We present a framework to maximize the final model accuracy by strategically adjusting the compression each iteration.
  arXiv  Detail & Related papers  (2021-12-13T14:26:15Z)
• Faster Non-Convex Federated Learning via Global and Local Momentum [57.52663209739171]
  textttFedGLOMO is the first (first-order) FLtexttFedGLOMO algorithm. Our algorithm is provably optimal even with communication between the clients and the server.
  arXiv  Detail & Related papers  (2020-12-07T21:05:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.