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Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications

arXiv 2507.11183 ↗First posted 15 Jul 2025cs.LGcited by 0

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Abstract (arXiv)

Federated learning is a machine learning approach that enables multiple devices (i.e., agents) to train a shared model cooperatively without exchanging raw data. This technique keeps data localized on user devices, ensuring privacy and security, while each agent trains the model on their own data and only shares model updates. The communication overhead is a significant challenge due to the frequent exchange of model updates between the agents and the central server. In this paper, we propose a communication-efficient federated learning scheme that utilizes low-rank approximation of neural network gradients and quantization to significantly reduce the network load of the decentralized learning process with minimal impact on the model's accuracy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 15 Jul 2025.

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9 of 24 references.

References · 9

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  6. 2019Distributed Learning with Compressed Gradient Differencescited by 112
  7. 2019Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobiancited by 67
  8. 2015Communication Complexity of Distributed Convex Learning and Optimizationcited by 60
  9. 2019Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagationcited by 9

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  1. 2021FedDQ: Communication-Efficient Federated Learning with Descending Quantization
  2. 2024FedFQ: Federated Learning with Fine-Grained Quantization
  3. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
  4. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  5. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  6. 2024FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
  7. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
  8. 2025FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning