Document Type
Article
Publication Date
2-1-2024
Abstract
Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.
Publication Source (Journal or Book title)
IEEE Transactions on Wireless Communications
First Page
1228
Last Page
1242
Recommended Citation
Li, L., Huang, C., Shi, D., Wang, H., Zhou, X., Shu, M., & Pan, M. (2024). Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation. IEEE Transactions on Wireless Communications, 23 (2), 1228-1242. https://doi.org/10.1109/TWC.2023.3287549
