FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems

• URL: http://arxiv.org/abs/2601.04587v1
• Date: Thu, 08 Jan 2026 04:35:28 GMT
• Title: FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems
• Abstract summary: FedKDX is a learning framework that addresses limitations in healthcare AI through Negative Knowledge Distillation (NKD)<n>The framework integrates traditional knowledge distillation, contrastive learning, and NKD--within a unified architecture that maintains privacy while reducing communication costs.
• Score: 1.7499351967216341
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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