Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
Huayan Guo, Yifan Zhu, Haoyu Ma, Vincent K. N. Lau, Kaibin Huang, Xiaofan Li, Huabin Nong, Mingyu Zhou
Abstract
In this paper, we develop an orthogonal frequency-division multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, the local gradients in massive Internet of things (IoT) devices are modulated by an analog waveform and are then transmitted using the same wireless resources. To this end, achieving perfect waveform superposition is the key challenge, which is difficult due to the existence of frame timing offset (TO) and carrier frequency offset (CFO). In order to address these issues, we propose a two-stage waveform pre-equalization technique with a customized multiple access protocol that can estimate and then mitigate the TO and CFO for the OTA aggregation. Based on the proposed solution, we develop a hardware transceiver and application software to train a real-world FL task, which learns a deep neural network to predict the received signal strength with the global positioning system information. Experiments verify that the proposed OTA aggregation solution can achieve comparable performance to offline learning procedures with high prediction accuracy.