Learned Digital Codes for Over-the-Air Federated Learning
This addresses the challenge of robust and efficient wireless uplink for federated edge learning, particularly for IoT devices, with incremental improvements over existing methods.
The paper tackles the problem of over-the-air federated learning in low-SNR conditions by proposing a learned digital framework, achieving an extension of reliable operation by more than 7 dB and improved global model convergence across all SNR levels.
Federated edge learning (FEEL) enables distributed model training across wireless devices without centralising raw data, but deployment is constrained by the wireless uplink. A promising direction is over-the-air (OTA) aggregation, which merges communication with computation. Existing digital OTA methods can achieve either strong convergence or robustness to noise, but struggle to achieve both simultaneously, limiting performance in low signal-to-noise ratios (SNRs) where many IoT devices operate. This work proposes a learnt digital OTA framework that extends reliable operation into low-SNR conditions while maintaining the same uplink overhead as state-of-the-art. The proposed method combines an unrolled decoder with a jointly learnt unsourced random access codebook. Results show an extension of reliable operation by more than 7 dB, with improved global model convergence across all SNR levels, highlighting the potential of learning-based design for FEEL.