mlsanityData to 7 Oct 2026

Service Delay Minimization for Federated Learning over Mobile Devices

arXiv 2205.09868 ↗First posted 19 May 2022cs.LG cs.DCcited by 9

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Abstract (arXiv)

Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communication delay over training rounds. To minimize the service delay of FL, simply reducing local computing/communication delay independently is not enough. The delay trade-off between local computing and wireless communications must be considered. Besides, we empirically study the impacts of local computing control and compression strategies (i.e., the number of local updates, weight quantization, and gradient quantization) on computing, communication and service delays. Based on those trade-off observation and empirical studies, we develop an optimization scheme to minimize the service delay of FL over heterogeneous devices. We establish testbeds and conduct extensive emulations/experiments to verify our theoretical analysis. The results show that SDEFL reduces notable service delay with a small accuracy drop compared to peer designs.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 26 of its 52 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

26 of 52 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  10. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2018LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networkscited by 272
14 more
  1. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  2. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 179
  3. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  4. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  5. 2017Training Quantized Nets: A Deeper Understandingcited by 74
  6. 2020Cost-Effective Federated Learning Designcited by 61
  7. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 45
  8. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  9. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  10. 2021CPT: Efficient Deep Neural Network Training via Cyclic Precisioncited by 19
  11. 2021FedDQ: Communication-Efficient Federated Learning with Descending Quantizationcited by 18
  12. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  13. 2021Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approachcited by 11
  14. 2020A flexible framework for communication-efficient machine learning: from HPC to IoTcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 2; 2026: 1.

9 papers.

  1. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  2. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  3. 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
  4. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  5. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  6. 2025Efficient Split Federated Learning for Large Language Models over Communication Networkscited by 1
  7. 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devicescited by 0
  8. 2023Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learningcited by 0
  9. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0