Fluid Antenna Assisted Over-the-Air Computation for Federated Learning Systems
Under review.
This paper has been submitted to the IEEE Transactions on Wireless Communications and is presently undergoing peer review.
Publications
AirComp-Assisted Asynchronous Federated Learning for UAV Swarms: A Self-Adaptive Aggregation Scheme to Tackle Model Staleness
Published in IEEE Transactions on Wireless Communications, 2026
Federated learning (FL) is a promising paradigm for collaborative intelligence in the low-altitude economy, enabling unmanned aerial vehicle (UAV) swarms to perform deep learning tasks (e.g., logistics, emergency rescue) while preserving data sovereignty. However, limited communication channels and heterogeneous computation capabilities among UAVs cause significant FL aggregation delays. To reduce convergence time, we propose an asynchronous FL (AFL) framework for UAV swarms, which integrates over-the-air computation (AirComp) to improve communication efficiency via simultaneous transmission. To address signal distortion in AirComp, we formulate an objective function and solve it using an aggregation scheduling algorithm, which transforms the nonconvex problem into two convex subproblems tackled via alternating optimization, to derive optimal aggregation strategies and beamforming vectors. Moreover, to mitigate model staleness in AFL, which causes gradient divergence and slow convergence, we propose a self-adaptive aggregation scheme with staleness awareness, enabling UAVs to adjust local models autonomously without requiring information from other UAVs. Simulation results show that our scheme reduces staleness impact and leverages stale parameters, helping AFL outperform synchronous FL in convergence speed and accuracy. Overall, our study presents an effective AFL framework, a fast aggregation scheduling algorithm, and a self-adaptive aggregation scheme for UAV swarms, accelerating global model convergence while reducing energy expenditure.
Recommended citation: Y. Huang, X. Li, L. Zhang and M. Peng, "AirComp-Assisted Asynchronous Federated Learning for UAV Swarms: A Self-Adaptive Aggregation Scheme to Tackle Model Staleness," in IEEE Transactions on Wireless Communications, vol. 25, pp. 17896-17910, May 2026.
Download Paper
Asynchronous Federated Learning via Over-the-Air Computation in LEO Satellite Networks
Published in IEEE Transactions on Wireless Communications, 2024
Owing to its ability to offer collaborative data utilization while ensuring data privacy, federated learning (FL) provides a promising paradigm to enable cooperative intelligent tasks across multiple low-earth orbit (LEO) satellites, such as carbon estimation, traffic surveillance, and forest fire detection. Although the advantages of pushing intelligence to satellites are multifold, limited communication channels along with the rigid global model aggregation conditions result in dramatic convergence delays. In order to reduce the convergence time, we propose an asynchronous FL framework in LEO satellite networks by exploiting multiple high-altitude platforms for model aggregation, where the advanced over-the-air computation (AirComp) transmission scheme is utilized for the sake of further reducing energy consumption. Considering the practical constraint of AirComp signal distortion, the objective function of optimizing FL performance is carefully formulated and solved by the proposed quantity-quality jointed linkage search algorithm. Simulation results demonstrate that our proposed asynchronous FL framework outperforms the conventional synchronous FL framework by a decline of 30.07% in convergence time at most. It also provides an average increase of 110% and 580%, respectively, in terms of throughput and energy efficiency in all scenarios considered. Overall, our study presents a beneficial asynchronous FL framework and a fast aggregation scheduling algorithm in LEO satellite networks, accelerating the convergence of the global model with reduced energy expenditure.
Recommended citation: Y. Huang, X. Li, M. Zhao, H. Li and M. Peng, "Asynchronous Federated Learning via Over-the-Air Computation in LEO Satellite Networks," in IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 19885-19901, Dec. 2024.
Download Paper
Damaged Road Extraction Based on Simulated Post-Disaster Remote Sensing Images
Published in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Damaged road extraction is a challenging task in the field of remote sensing. Some existing methods include the step to extract road from pre- and post-disaster remote sensing images of the same area. In practice, it often occurs that one of these two images is missing. To solve this problem, we use CoCosNet, the model for exemplar-based image translation, to translate pre-disaster images to simulated post-disaster ones. Then we use D-LinkNet, the state-of-the-art method in road extraction, to extract road from the pre- and post-disaster images of the same area. We extract damaged road area by comparing pre-disaster road masks with post-disaster ones and output the damage level by calculating the proportion of the damaged road area. Finally, we evaluate the damaged road extraction accuracy. Experimental results on simulated post-disaster images prove the effectiveness of the simulation method and the framework for damaged road extraction and damage level evaluation.
Recommended citation: Y. Huang, H. Wei, J. Yang and M. Wu, "Damaged Road Extraction Based on Simulated Post-Disaster Remote Sensing Images," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 4684-4687
Download Paper | Download Slides