FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914239954812928 |
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| author | Pham, Quang-Tu Vu, Hoang-Dieu Pham, Dinh-Dat Pham, Hieu H. |
| author_facet | Pham, Quang-Tu Vu, Hoang-Dieu Pham, Dinh-Dat Pham, Hieu H. |
| contents | This paper introduces FedKDX, a federated learning framework that addresses limitations in healthcare AI through Negative Knowledge Distillation (NKD). Unlike existing approaches that focus solely on positive knowledge transfer, FedKDX captures both target and non-target information to improve model generalization in healthcare applications. The framework integrates multiple knowledge transfer techniques--including traditional knowledge distillation, contrastive learning, and NKD--within a unified architecture that maintains privacy while reducing communication costs. Through experiments on healthcare datasets (SLEEP, UCI-HAR, and PAMAP2), FedKDX demonstrates improved accuracy (up to 2.53% over state-of-the-art methods), faster convergence, and better performance on non-IID data distributions. Theoretical analysis supports NKD's contribution to addressing statistical heterogeneity in distributed healthcare data. The approach shows promise for privacy-sensitive medical applications under regulatory frameworks like HIPAA and GDPR, offering a balanced solution between performance and practical implementation requirements in decentralized healthcare settings. The code and model are available at https://github.com/phamdinhdat-ai/Fed_2024. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_04587 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems Pham, Quang-Tu Vu, Hoang-Dieu Pham, Dinh-Dat Pham, Hieu H. Machine Learning Artificial Intelligence This paper introduces FedKDX, a federated learning framework that addresses limitations in healthcare AI through Negative Knowledge Distillation (NKD). Unlike existing approaches that focus solely on positive knowledge transfer, FedKDX captures both target and non-target information to improve model generalization in healthcare applications. The framework integrates multiple knowledge transfer techniques--including traditional knowledge distillation, contrastive learning, and NKD--within a unified architecture that maintains privacy while reducing communication costs. Through experiments on healthcare datasets (SLEEP, UCI-HAR, and PAMAP2), FedKDX demonstrates improved accuracy (up to 2.53% over state-of-the-art methods), faster convergence, and better performance on non-IID data distributions. Theoretical analysis supports NKD's contribution to addressing statistical heterogeneity in distributed healthcare data. The approach shows promise for privacy-sensitive medical applications under regulatory frameworks like HIPAA and GDPR, offering a balanced solution between performance and practical implementation requirements in decentralized healthcare settings. The code and model are available at https://github.com/phamdinhdat-ai/Fed_2024. |
| title | FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.04587 |