Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

2026-09-01

AI Summary

为解决AI教育系统中隐私保护与准确认知诊断的平衡问题，提出一种联邦推理框架，利用多个商业LLM API协作，并通过本地添加拉普拉斯噪声实现差分隐私。

Abstract

Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. To overcome this, we propose a federated inference framework in which several commercial LLM APIs collaborate without requiring access to raw student data or proprietary model internals. Using multiple federated entities, such as LLaMA-3.3-70B, GPT-4o-mini, and Claude-3-Haiku, our framework builds upon a heterogeneous multi-LLM architecture. The predictions generated by these entities are combined with epsilon-local differential privacy by adding Laplace noise locally to each entity's prediction output before aggregation, while residual-based aggregation mitigates model heterogeneity. Our approach is predicated on an honest-but-curious trust paradigm in which API providers are presumed not to abuse submitted queries, and our differential privacy mechanism shields the published diagnostic results from external inference. We conduct rigorous privacy-utility analysis showing strong privacy guarantees with minimal accuracy loss, and extensive real-world evaluations across three educational benchmarks confirm the framework's practical usability and cross-domain generalizability.

Problem

• privacy preservation
• cognitive diagnosis
• federated inference
• multi-LLM
• differential privacy

Innovation

• Federated Inference
• Heterogeneous Multi-LLM
• Differential Privacy
• Residual-based Aggregation