@inproceedings{boyapati-etal-2026-privacy,
title = "Privacy-Preserving Heterogeneous Multi-{LLM} Federated Inference for Cognitive Diagnosis",
author = "Boyapati, Yagna Manasa and
Yu, Chong and
Jiang, Tianyu and
Zhan, Justin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1318/",
pages = "27913--27928",
ISBN = "979-8-89176-462-0",
abstract = "Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. In order 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 the heterogeneous multi-LLM architecture. The predictions generated by these entities are combined with {\ensuremath{\varepsilon}}-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."
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<abstract>Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. In order 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 the heterogeneous multi-LLM architecture. The predictions generated by these entities are combined with \ensuremathǎrepsilon-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.</abstract>
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%0 Conference Proceedings
%T Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis
%A Boyapati, Yagna Manasa
%A Yu, Chong
%A Jiang, Tianyu
%A Zhan, Justin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F boyapati-etal-2026-privacy
%X Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. In order 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 the heterogeneous multi-LLM architecture. The predictions generated by these entities are combined with \ensuremathǎrepsilon-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.
%U https://aclanthology.org/2026.findings-emnlp.1318/
%P 27913-27928
Markdown (Informal)
[Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis](https://aclanthology.org/2026.findings-emnlp.1318/) (Boyapati et al., Findings 2026)
ACL