1.
Zheng, Zijian; Deng, Yansha; Yi, Wenqiang; Shin, Hyundong; Nallanathan, Arumugam
Over-the-Air Computation Enabled Semi-Asynchronous Wireless Federated Learning Journal Article
In: IEEE Transactions on Communications, pp. 1–1, 2025, ISSN: 1558-0857.
Abstract | Links | BibTeX | Tags: aggregation optimization, Atmospheric modeling, Computational modeling, Convergence, Federated learning, Noise, Optimization, over-the-air computation, Semi-asynchronous federated learning, Servers, Synchronization, Training, Wireless networks
@article{zheng_over–air_2025,
title = {Over-the-Air Computation Enabled Semi-Asynchronous Wireless Federated Learning},
author = {Zijian Zheng and Yansha Deng and Wenqiang Yi and Hyundong Shin and Arumugam Nallanathan},
url = {https://ieeexplore.ieee.org/document/11048956},
doi = {10.1109/TCOMM.2025.3582727},
issn = {1558-0857},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Communications},
pages = {1–1},
abstract = {The emerging field of federated learning (FL) holds significant promise for advancing edge intelligence while preserving data privacy. However, as FL systems scale or become more heterogeneous, challenges such as spectrum scarcity and the straggler problem arise. To address these issues, this paper proposes SA-AirFed, a semi-asynchronous FL architecture compatible with Over-the-Air Computation (AirComp). We develop an efficient scheduling scheme that meets AirComp’s requirements and analyze the factors affecting convergence under the Lipschitz-Smooth condition. Building on insights from the convergence analysis, we design an adaptive algorithm that mitigates staleness from semi-asynchronous aggregation and noise from AirComp by dynamically adjusting aggregation weights, formulated as a convex quadratic programming problem. Experimental results on MNIST and CIFAR-10 demonstrate that SA-AirFed significantly reduces wall-clock training time while achieving greater robustness compared to baseline models.},
keywords = {aggregation optimization, Atmospheric modeling, Computational modeling, Convergence, Federated learning, Noise, Optimization, over-the-air computation, Semi-asynchronous federated learning, Servers, Synchronization, Training, Wireless networks},
pubstate = {published},
tppubtype = {article}
}
The emerging field of federated learning (FL) holds significant promise for advancing edge intelligence while preserving data privacy. However, as FL systems scale or become more heterogeneous, challenges such as spectrum scarcity and the straggler problem arise. To address these issues, this paper proposes SA-AirFed, a semi-asynchronous FL architecture compatible with Over-the-Air Computation (AirComp). We develop an efficient scheduling scheme that meets AirComp’s requirements and analyze the factors affecting convergence under the Lipschitz-Smooth condition. Building on insights from the convergence analysis, we design an adaptive algorithm that mitigates staleness from semi-asynchronous aggregation and noise from AirComp by dynamically adjusting aggregation weights, formulated as a convex quadratic programming problem. Experimental results on MNIST and CIFAR-10 demonstrate that SA-AirFed significantly reduces wall-clock training time while achieving greater robustness compared to baseline models.