Kavli Affiliate: Jia Liu
| First 5 Authors: Jiayu Mao, Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener
| Summary:
Over-the-air federated learning (OTA-FL) has emerged as an efficient
mechanism that exploits the superposition property of the wireless medium and
performs model aggregation for federated learning in the air. OTA-FL is
naturally sensitive to wireless channel fading, which could significantly
diminish its learning accuracy. To address this challenge, in this paper, we
propose an OTA-FL algorithm called CHARLES (channel-quality-aware over-the-air
local estimating and scaling). Our CHARLES algorithm performs channel state
information (CSI) estimation and adaptive scaling to mitigate the impacts of
wireless channel fading. We establish the theoretical convergence rate
performance of CHARLES and analyze the impacts of CSI error on the convergence
of CHARLES. We show that the adaptive channel inversion scaling scheme in
CHARLES is robust under imperfect CSI scenarios. We also demonstrate through
numerical results that CHARLES outperforms existing OTA-FL algorithms with
heterogeneous data under imperfect CSI.
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