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Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning
In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources.
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation
In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI).
Networking and Internet Architecture
