arXiv Artificial Intelligence

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

7 days ago 04.09.2026 07:00 👁 4 views
Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

Quick summary

arXiv:2609.02947v1 Announce Type: cross 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-loca

Key takeaways

  • arXiv:2609.02947v1 Announce Type: cross 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.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗