mlsanityData to 14 Oct 2026

FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization

arXiv 2406.18156 ↗First posted 26 Jun 2024cs.LG cs.DC cs.NIcited by 5

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Abstract (arXiv)

Federated learning (FL) is a powerful machine learning paradigm which leverages the data as well as the computational resources of clients, while protecting clients' data privacy. However, the substantial model size and frequent aggregation between the server and clients result in significant communication overhead, making it challenging to deploy FL in resource-limited wireless networks. In this work, we aim to mitigate the communication overhead by using quantization. Previous research on quantization has primarily focused on the uplink communication, employing either fixed-bit quantization or adaptive quantization methods. In this work, we introduce a holistic approach by joint uplink and downlink adaptive quantization to reduce the communication overhead. In particular, we optimize the learning convergence by determining the optimal uplink and downlink quantization bit-length, with a communication energy constraint. Theoretical analysis shows that the optimal quantization levels depend on the range of model gradients or weights. Based on this insight, we propose a decreasing-trend quantization for the uplink and an increasing-trend quantization for the downlink, which aligns with the change of the model parameters during the training process. Experimental results show that, the proposed joint uplink and downlink adaptive quantization strategy can save up to 66.7% energy compared with the existing schemes.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 40 of its 60 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

40 of 60 references.

References · 40

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28 more
  1. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2025FedHQ: Hybrid Runtime Quantization for Federated Learningcited by 2
  2. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networkscited by 1
  3. 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantizationcited by 1
  4. 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
  5. 2026Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacycited by 1

Similar, not cited

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  1. 2022Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization
  2. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  3. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  4. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  5. 2025Lightweight Federated Learning over Wireless Edge Networks
  6. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
  7. 2024FedFQ: Federated Learning with Fine-Grained Quantization
  8. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing