Scalable and Reliable Over-the-Air Federated Edge Learning
This addresses a critical scalability issue in federated learning for edge devices, though it is an incremental improvement over existing channel coding approaches.
The paper tackles the communication bottleneck in federated edge learning by proposing a digital lattice-based code construction for over-the-air computation, achieving constant error-correction capabilities regardless of the number of clients, unlike prior methods that degrade with more clients.
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.