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Backdoor attacks pose a serious threat to the secure deployment of text-to-image (T2I) diffusion models. Existing defenses typically detect backdoors from specific abnormal patterns in internal representations, which may limit their generalizability with the emergence of increasingly diverse attack mechanisms. In this...
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...
Federated learning (FL) is susceptible to poisoning attacks, where malicious clients manipulate local data or models to disrupt training. The system and data heterogeneity inherent in practical FL systems exacerbates these vulnerabilities, rendering existing defense mechanisms ineffective or infeasible. Specifically, d...
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