Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learning
Lizhao You, Xinbo Zhao, Rui Cao, Yulin Shao, and Liqun Fu

TL;DR
This paper introduces a novel digital over-the-air computation system for wireless federated learning using OFDM, which overcomes phase asynchrony issues inherent in analog systems, and demonstrates its effectiveness through real-world prototype experiments.
Contribution
The paper develops the first digital AirComp system for OFDM-based wireless federated learning, including a MAC protocol and joint decoding methods, validated by a prototype and simulations.
Findings
Digital AirComp outperforms analog systems at all SNRs.
The system approaches optimal performance at SNR ≥ 6 dB.
Analog AirComp is highly sensitive to phase asynchrony in practice.
Abstract
This paper presents the first orthogonal frequency-division multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for wireless federated edge learning, where multiple edge devices transmit model data simultaneously using non-orthogonal OFDM subcarriers, and the edge server aggregates data directly from the superimposed signal. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog transmission for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation for phase-asynchronous multi-user OFDM systems. To realize a digital AirComp system, we develop a medium access control (MAC) protocol that allows simultaneous transmissions from different users using non-orthogonal OFDM subcarriers,…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Cooperative Communication and Network Coding
