In this paper, we first discover that the high within-class similarity in condensed datasets necessitates the use of large-scale soft labels.

Federated Learning (FL) coordinates with numerous heterogeneous devices to collaboratively train a shared model while preserving user privacy.

Despite being powerful and well-understood, the kernel ridge regression suffers from the costly computation when dealing with large datasets, since generally implementation of Eq. (1) requires O(n3) running time.