# Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach

arXiv: https://arxiv.org/abs/2209.06623 · HTML: https://mlsanity.com/paper/2209.06623/

## 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 (most cited first, 21 of 21)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness](https://mlsanity.com/paper/2004.00490/) (2020)
- [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [Toward Understanding the Impact of Staleness in Distributed Machine Learning](https://mlsanity.com/paper/1810.03264/) (2018)
- [Federated Learning and Wireless Communications](https://mlsanity.com/paper/2005.05265/) (2020)
- [Cell-Free Massive MIMO for Wireless Federated Learning](https://mlsanity.com/paper/1909.12567/) (2019)
- [Federated Learning over Energy Harvesting Wireless Networks](https://mlsanity.com/paper/2106.08809/) (2021)
- [3D UAV Trajectory and Communication Design for Simultaneous Uplink and Downlink Transmission](https://mlsanity.com/paper/2001.00351/) (2020)
- [Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing](https://mlsanity.com/paper/2203.12738/) (2022)

## Built on by (most cited first, 10 of 10)

- [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023)
- [Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices](https://mlsanity.com/paper/2412.20004/) (2024)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Value of Information and Timing-aware Scheduling for Federated Learning](https://mlsanity.com/paper/2312.10512/) (2023)
- [Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning](https://mlsanity.com/paper/2406.17470/) (2024)
- [Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning](https://mlsanity.com/paper/2501.01828/) (2025)
- [Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks](https://mlsanity.com/paper/2403.03157/) (2024)
- [Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks](https://mlsanity.com/paper/2501.01584/) (2025)
- [Balancing Client Participation in Federated Learning Using AoI](https://mlsanity.com/paper/2505.05099/) (2025)
- [Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning](https://mlsanity.com/paper/2607.13119/) (2026)

## 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.