Chenghui Wei

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement

TriShield is presented, a three-layer deterministic defense that completely prevents NeuroImprint-style reconstruction with zero model utility loss and no additional communication rounds, and it is proved theoretically that after Layers 2 and 3, the mutual information between the uploaded gradient and any individual training sample is zero.