AI RESEARCH
Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
arXiv CS.LG
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ArXi:2506.21744v2 Announce Type: replace Item Response Theory (IRT) models are widely used to estimate respondents' latent abilities and calibrate item difficulty. Traditional IRT estimation typically requires centralizing all raw responses, raising privacy and governance concerns. We To provide formal protection, we further develop FedIRT-DP, a user-level differentially private extension. Each site computes per-student gradients, clips them to a fixed norm, and shares only masked sums; the server adds calibrated Gaussian noise and performs MAP updates.