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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 T
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