mlsanityData to 14 Oct 2026

AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy

arXiv 2308.00258 ↗First posted 1 Aug 2023cs.LG cs.DCcited by 7

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Abstract (arXiv)

The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typically arising from the transmission of large-scale models. Existing adaptive quantization methods, designed to mitigate these overheads, operate under the impractical assumption of uniform device participation in every training round. Additionally, these methods are limited in their adaptability due to the necessity of manual quantization level selection and often overlook biases inherent in local devices' data, thereby affecting the robustness of the global model. In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL. AQUILA integrates a sophisticated device selection method that prioritizes the quality and usefulness of device updates. Utilizing the exact global model stored by devices, it enables a more precise device selection criterion, reduces model deviation, and limits the need for hyperparameter adjustments. Furthermore, AQUILA presents an innovative quantization criterion, optimized to improve communication efficiency while assuring model convergence. Our experiments demonstrate that AQUILA significantly decreases communication costs compared to existing methods, while maintaining comparable model performance across diverse non-homogeneous FL settings, such as Non-IID data and heterogeneous model architectures.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 1 Aug 2023.

Builds on

22 of 38 references.

References · 22

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10 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2023Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Promptcited by 1
  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. 2025FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensingcited by 1
  5. 2025Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Servicescited by 1
  6. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  7. 2026Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detectioncited by 0

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  1. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions
  2. 2026Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy
  3. 2026Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis
  4. 2021Federated Learning with Downlink Device Selection
  5. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  6. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  7. 2024A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning
  8. 2022Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices