Blind Federated Learning with Low-Cost Analog-to-Digital Converters
Büşra Tegin, Tolga M. Duman · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
We study federated learning over wireless channels where a massive dataset is distributed across independent workers which compute their local gradients based on their own datasets. Workers send their gradients through a multipath fading multiple access channel with orthogonal frequency division multiplexing to mitigate the frequency selectivity of the channel. We assume that there is no channel state information (CSI) at the workers, and the parameter server (PS) employs multiple antennas to align the received signals. To reduce the power consumption and hardware costs, we employ complex-valued low-resolution analog-to-digital converters (ADCs) at the receiver side, and study the effects of practical low-cost ADCs on the learning performance. Our results show that the impairments caused by low-resolution ADCs, including those of one-bit ADCs, do not prevent the convergence of the federated learning algorithm, and the multipath channel effects vanish when a sufficient number of antennas are used at the PS.