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FairQuanti: Enhancing Fairness in Deep Neural Network Quantization via Neuron Role Contribution

FairQuanti: Enhancing Fairness in Deep Neural Network Quantization via Neuron Role Contribution The increasing complexity of deep neural networks (DNNs) poses significant resource challenges for edge devices, prompting the development of compression technologies like model quantization. However, while improving model efficiency, quantization can introduce or perpetuate the original model’s bias. Existing debiasing methods for quantized models often incur additional costs. To address this issue, we propose FairQuanti, a novel quantization approach that leverages neuron role contribution to achieve fairness. By distinguishing between biased and normal neurons, FairQuanti employs mixed precision quantization to mitigate model bias during the quantization process. FairQuanti has four key differences from previous studies: (1) Neuron Roles - It formally defines biased and normal neuron roles, establishing a framework for feasible model quantization and bias mitigation; (2) Effectiveness - It introduces a fair quantization strategy that discriminatively quantizes neuron roles, balancing model accuracy and fairness through Bayesian optimization; (3) Generality - It applies to both structured and unstructured data across various quantization bit levels; (4) Robustness - It demonstrates resilience against adaptive attacks. Extensive experiments on five datasets (three structured and two unstructured) using five different models validate FairQuanti’s superior performance against eight baseline methods. Specifically, fairness metrics such as demographic parity (DP) improve by approximately 1.03 times, and the demographic parity ratio (DPR) improves by approximately 1.51 times compared to the baselines, with an average accuracy loss of less than 7.5% at 8-bit quantization. FairQuanti presents a promising solution for deploying fair and efficient deep models on resource-constrained devices and holds potential for application in large language models to reduce size and computational demands while minimizing bias. Our source code is available at https://github.com/Caozq2/FairQuanti. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Privacy and Security (TOPS) Association for Computing Machinery

FairQuanti: Enhancing Fairness in Deep Neural Network Quantization via Neuron Role Contribution

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References (68)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ISSN
2471-2566
eISSN
2471-2574
DOI
10.1145/3744560
Publisher site
See Article on Publisher Site

Abstract

The increasing complexity of deep neural networks (DNNs) poses significant resource challenges for edge devices, prompting the development of compression technologies like model quantization. However, while improving model efficiency, quantization can introduce or perpetuate the original model’s bias. Existing debiasing methods for quantized models often incur additional costs. To address this issue, we propose FairQuanti, a novel quantization approach that leverages neuron role contribution to achieve fairness. By distinguishing between biased and normal neurons, FairQuanti employs mixed precision quantization to mitigate model bias during the quantization process. FairQuanti has four key differences from previous studies: (1) Neuron Roles - It formally defines biased and normal neuron roles, establishing a framework for feasible model quantization and bias mitigation; (2) Effectiveness - It introduces a fair quantization strategy that discriminatively quantizes neuron roles, balancing model accuracy and fairness through Bayesian optimization; (3) Generality - It applies to both structured and unstructured data across various quantization bit levels; (4) Robustness - It demonstrates resilience against adaptive attacks. Extensive experiments on five datasets (three structured and two unstructured) using five different models validate FairQuanti’s superior performance against eight baseline methods. Specifically, fairness metrics such as demographic parity (DP) improve by approximately 1.03 times, and the demographic parity ratio (DPR) improves by approximately 1.51 times compared to the baselines, with an average accuracy loss of less than 7.5% at 8-bit quantization. FairQuanti presents a promising solution for deploying fair and efficient deep models on resource-constrained devices and holds potential for application in large language models to reduce size and computational demands while minimizing bias. Our source code is available at https://github.com/Caozq2/FairQuanti.

Journal

ACM Transactions on Privacy and Security (TOPS) – Association for Computing Machinery

Published: Aug 23, 2025

Keywords: Deep neural network

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