Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation

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. To address those challenges, in this paper, we propose a novel multibit 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. L. Li is with the School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China (e-mail: liliang1127@bupt.edu.cn). C. Huang, D. Shi and M. Pan are with the Electrical and Computer Engineering Department, University of Houston, TX, 77004, USA (e-mail: chuang25@uh.edu, H. Wang is with the Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA (e-mail: haowang@lsu.edu).