mlsanityData to 14 Oct 2026

GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems

arXiv 2306.07497 ↗First posted 13 Jun 2023cs.LG cs.DCcited by 1

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Abstract (arXiv)

The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers. Specifically, we first present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely GQFedWAvg. Specifically, GQFedWAvg applies the proposed quantization scheme to quantize wisely chosen model update-related vectors and adopts a generalized mini-batch stochastic gradient descent (SGD) method with the weighted average local model updates in global model aggregation. Besides, GQFedWAvg has several adjustable algorithm parameters to flexibly adapt to the computing and communication resources at the server and workers. We also analyze the convergence of GQFedWAvg. Next, we optimize the algorithm parameters of GQFedWAvg to minimize the convergence error under the time and energy constraints. We successfully tackle the challenging non-convex problem using general inner approximation (GIA) and multiple delicate tricks. Finally, we interpret GQFedWAvg's function principle and show its considerable gains over existing FL algorithms using numerical results.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 13 Jun 2023.

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21 of 39 references.

References · 21

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  2. 2021Convex optimizationcited by 2,515
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  12. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
9 more
  1. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  2. 2018Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDcited by 77
  3. 2018Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communicationcited by 51
  4. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  5. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  6. 2021Fast Federated Learning by Balancing Communication Trade-Offscited by 18
  7. 2021Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCAcited by 3
  8. 2021Wyner-Ziv Gradient Compression for Federated Learningcited by 3
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Citing papers per year: 2024: 1.

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  1. 2024Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigmcited by 13

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  2. 2022Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization
  3. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  4. 2024FedFQ: Federated Learning with Fine-Grained Quantization
  5. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks
  6. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  7. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
  8. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design