Scalable and Reliable Over-the-Air Federated Edge Learning
<|reference_start|>Scalable and Reliable Over-the-Air Federated Edge Learning: 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.<|reference_end|>
@article{egger2024scalable,
    title={Scalable and Reliable Over-the-Air Federated Edge Learning},
    author={Maximilian Egger, Christoph Hofmeister, Cem Kaya, Rawad Bitar, Antonia
  Wachter-Zeh},
    journal={arXiv preprint arXiv:2407.11807},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11807},
    primaryClass={cs.IT cs.DC cs.LG math.IT}
}