Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach

Over-the-air (OTA) computation has recently emerged as a communication-efficient Federated Learning (FL) paradigm to train machine learning models over wireless networks. However, its performance is limited by the device with the worst SNR, resulting in fast yet noisy updates. On the other hand, allocating orthogonal resource blocks (RB) to individual devices via digital channels mitigates the noise problem, at the cost of increased communication latency. In this paper, we address this discrepancy and present ADFL, a novel Analog-Digital FL scheme: …

analog communication computation cs.it cs.lg devices digital eess.sp federated learning machine machine learning machine learning models math.it networks paradigm performance scheduling train updates via wireless