Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. However, under extremely non-IID settings, existing data-free methods often generate low-quality data that suffers from severe semantic misalignment with ground-truth labels. To overcome these issues, we propose a novel Federated Model Inversion and Token Relabel (FedMITR) framework, which trains the global model by fully exploiting all patches of synthetic images. Specifically, FedMITR employs sparse model inversion during data generation, selectively inverting semantic foregrounds while halting the inversion of uninformative backgrounds. To addr...
| Authors | Li Shen, Xiaolei Hao, Qinglun Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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