Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel... (read more)

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