Search papers, labs, and topics across Lattice.
2
0
3
1
Exposing intermediate representations in federated split learning can leak client data, but a new defense cuts structural similarity in reconstructed images by up to 40% without hurting model performance.
By synthesizing data with a diffusion model and selectively retaining informative samples, OSI-FL achieves state-of-the-art performance in class-incremental and domain-incremental federated learning scenarios while minimizing communication costs.
