FedOL builds a larger server model from one round of client prediction sharing, using confidence-weighted voting and iterative pseudo-label refinement to beat federated distillation baselines on CIFAR-100.
Language models are few-shot learners,
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Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
FedOL builds a larger server model from one round of client prediction sharing, using confidence-weighted voting and iterative pseudo-label refinement to beat federated distillation baselines on CIFAR-100.