A wireless FL scheduling algorithm that minimizes collective gradient divergence by balancing group-level data distribution (WEMD) and sampling variance achieves higher CIFAR-10 accuracy with up to 41.8% fewer scheduled devices.
Edge learning with timeliness constraints: Challenges and solutions,
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FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
A wireless FL scheduling algorithm that minimizes collective gradient divergence by balancing group-level data distribution (WEMD) and sampling variance achieves higher CIFAR-10 accuracy with up to 41.8% fewer scheduled devices.