FedMITR uses sparse model inversion and token relabeling to improve one-shot federated learning with ViTs under non-IID conditions, delivering a tighter generalization bound via algorithmic stability analysis and better empirical performance.
According to Lemma 3, the expected norm of the error signal from soft labels is strictly smaller than that from hard labels due to variance reduction:E[∥δ sof t∥]≪E[∥δ hard∥]
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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
FedMITR uses sparse model inversion and token relabeling to improve one-shot federated learning with ViTs under non-IID conditions, delivering a tighter generalization bound via algorithmic stability analysis and better empirical performance.