Scalable and Reliable Over-the-Air Federated Edge Learning
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Abstract (arXiv)
Federated edge learning (FEEL) has emerged as a core paradigm for large-scale optimization. However, FEEL still suffers from a communication bottleneck due to the transmission of high-dimensional model updates from the clients to the federator. Over-the-air computation (AirComp) leverages the additive property of multiple-access channels by aggregating the clients' updates over the channel to save communication resources. While analog uncoded transmission can benefit from the increased signal-to-noise ratio (SNR) due to the simultaneous transmission of many clients, potential errors may severely harm the learning process for small SNRs. To alleviate this problem, channel coding approaches were recently proposed for AirComp in FEEL. However, their error-correction capability degrades with an increasing number of clients. We propose a digital lattice-based code construction with constant error-correction capabilities in the number of clients, and compare to nested-lattice codes, well-known for their optimal rate and power efficiency in the point-to-point AWGN channel.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 16 Jul 2024.
Builds on
13 of 29 references.
References · 13
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
- 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
- 2015Deep Learning with Limited Numerical Precisioncited by 376
- 2018Federated Learning via Over-the-Air Computationcited by 219
- 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
- 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learningcited by 13
- 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 10
- 2023ChannelComp: A General Method for Computation by Communicationscited by 10
- 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Controlcited by 9
- 2022Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learningcited by 6
- 2023Joint Design of Coding and Modulation for Digital Over-the-Air Computationcited by 4
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Citing papers per year: 2025: 1.
1 paper.