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FISGuard: Defending Against Membership Inference via Fixed Input Subspaces

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

As large language models are increasingly adopted in federated learning, protecting user privacy while performing parameter-efficient fine-tuning on distributed private data has become an important challenge. Although clients only share gradients instead of directly uploading raw data, the shared gradients may still leak membership information about training samples. ProjRes (S&P, 2026) further increases this risk: with less information and without accessing model outputs, an attacker can effectively distinguish members from non-members solely based on the projection residual between a candida

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