mlsanityData to 14 Oct 2026

Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach

arXiv 2209.06623 ↗First posted 14 Sep 2022cs.LGcited by 10

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Abstract (arXiv)

This paper studies issues that arise with respect to the joint optimization for convergence time in federated learning over wireless networks (FLOWN). We consider the criterion and protocol for selection of participating devices in FLOWN under the energy constraint and derive its impact on device selection. In order to improve the training efficiency, age-of-information (AoI) enables FLOWN to assess the freshness of gradient updates among participants. Aiming to speed up convergence, we jointly investigate global loss minimization and latency minimization in a Stackelberg game based framework. Specifically, we formulate global loss minimization as a leader-level problem for reducing the number of required rounds, and latency minimization as a follower-level problem to reduce time consumption of each round. By decoupling the follower-level problem into two sub-problems, including resource allocation and sub-channel assignment, we achieve an optimal strategy of the follower through monotonic optimization and matching theory. At the leader-level, we derive an upper bound of convergence rate and subsequently reformulate the global loss minimization problem and propose a new age-of-update (AoU) based device selection algorithm. Simulation results indicate the superior performance of the proposed AoU based device selection scheme in terms of the convergence rate, as well as efficient utilization of available sub-channels.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 21 of its 48 references that are in the map. First posted on arXiv 14 Sep 2022.

Builds on

21 of 48 references.

References · 21

  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  2. 2021Convex optimizationcited by 2,515
  3. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  4. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  7. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  8. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  9. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  10. 2020Age of Information: An Introduction and Surveycited by 70
  11. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  12. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
9 more
  1. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  2. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  3. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  4. 2018Toward Understanding the Impact of Staleness in Distributed Machine Learningcited by 29
  5. 2020Federated Learning and Wireless Communicationscited by 27
  6. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  7. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  8. 20203D UAV Trajectory and Communication Design for Simultaneous Uplink and Downlink Transmissioncited by 2
  9. 2022Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computingcited by 2

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Cited by year (2026 to 14 Oct)

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

10 papers.

  1. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  2. 2024Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devicescited by 5
  3. 2023Collaborative Learning with a Drone Orchestratorcited by 3
  4. 2023Value of Information and Timing-aware Scheduling for Federated Learningcited by 3
  5. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  6. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  7. 2024Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networkscited by 2
  8. 2025Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networkscited by 0
  9. 2025Balancing Client Participation in Federated Learning Using AoIcited by 0
  10. 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learningcited by 0