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Sigil, a server-enforced watermarking framework for U-SFL, defines a secret watermark constraint in the server-visible activation space and embeds the watermark into client-side models by injecting a watermark gradient into the gradients returned during training.
Split federated learning (SFL) is renowned for its low computational overhead, extremely suitable for resource-constrained edge computing scenarios while inheriting privacy-preserving characteristics from federated learning (FL). In this framework, clients employ lightweight models to process private data locally and t...
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