Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for simultaneous model aggregation. Most existing work struggles with heterogeneous fading channels. These approaches either enforce unbiased up...
Ming Xiang, Nicolò Michelusi, Y. Eldar et al.
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