A cluster-aware over-the-air FL framework for energy-harvesting devices uses user clusters both to schedule diverse participants for a fair global model and to train personalized models per cluster, with convergence bounds and simulations on MNIST, FMNIST, and CIFAR-10.
Federated learning for Internet of Things: A com- prehensive survey,
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Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization
A cluster-aware over-the-air FL framework for energy-harvesting devices uses user clusters both to schedule diverse participants for a fair global model and to train personalized models per cluster, with convergence bounds and simulations on MNIST, FMNIST, and CIFAR-10.