FISGuard: Defending Against Membership Inference via Fixed Input Subspaces
FISGuard defends federated learning setups against membership inference during parameter-efficient fine-tuning — a growing concern as enterprises adopt LoRA-style distributed training on sensitive data.
Summary written by editorial AI · Source link below
arXiv:2608.27836v1 Announce Type: new Abstract: 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 withou
Editorial Analysis
As enterprises increasingly fine-tune LLMs on proprietary data in federated settings, membership inference defences become critical to preventing training-data leakage.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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