Client-Aware Security Adaptive Early Exit Vision Transformers for Heterogeneous Federated Learning
Federated learning (FL) trains a shared model across data-owning clients without centralizing raw data, but client data difficulty and compute budgets can differ. We study local early-exit Vision Transformer (ViT) inference, where an intermediate classifier may return a prediction before the final Transformer stage. The proposed method replaces one global acceptance threshold with a client-specific base threshold from label entropy and shallow-exit confidence, then adjusts each input using predictive entropy, confidence, top-two class margin, and exit depth. Aggregation uses only clients that updated a parameter. Security-aware routing reaches 88.70% CIFAR-10 accuracy with 32.0% depth sav...
| Authors | Sahar Refaei Ghalibafan, Samira Nazari, Christian Herglotz et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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