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
• Authors: Quang-Tu Pham, Hoang-Dieu Vu, Dinh-Dat Pham, Hieu H. Pham,
• 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.

Related papers

• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx [0.0]
  Federated Learning (FL) presents a groundbreaking approach for collaborative health research.<n>FL offers formal security guarantees when combined with Differential Privacy (DP)<n>An optimal operational region was identified on the privacy-utility frontier.
  arXiv  Detail & Related papers  (2025-08-06T20:47:50Z)
• Revisiting Medical Image Retrieval via Knowledge Consolidation [46.6989555659494]
  We propose a novel method to consolidate knowledge of hierarchical features and functions.<n>We introduce Depth-aware Representation Fusion (DaRF) and Structure-aware Contrastive Hashing (SCH)<n>Our method achieves a 5.6-38.9% improvement in mean Average Precision on the anatomical radiology dataset.
  arXiv  Detail & Related papers  (2025-03-12T13:16:42Z)
• Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification [0.0]
  This paper introduces a novel framework that integrates federated learning with quantum-inspired encryption techniques for dementia classification.<n>The framework offers significant implications for democratizing access to AI-driven dementia diagnostics in low- and middle-income countries.
  arXiv  Detail & Related papers  (2025-03-05T08:49:31Z)
• EPIC: Enhancing Privacy through Iterative Collaboration [4.199844472131922]
  Traditional machine learning techniques require centralized data collection and processing. Privacy, ownership, and stringent regulation issues exist when pooling medical data into centralized storage. The Federated learning (FL) approach overcomes such issues by setting up a central aggregator server and a shared global model.
  arXiv  Detail & Related papers  (2024-11-07T20:10:34Z)
• A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease [0.0]
  This paper introduces a HIPAA compliant framework that can train from distributed data. I then propose a multimodal vertical federated model for Alzheimer's Disease (AD) detection. The VFL architecture proposed herein offers a novel distributed architecture, enabling collaborative learning across diverse sources of medical data.
  arXiv  Detail & Related papers  (2023-12-15T22:09:04Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• Federated Offline Reinforcement Learning [55.326673977320574]
  We propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. We design the first federated policy optimization algorithm for offline RL with sample complexity. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed.
  arXiv  Detail & Related papers  (2022-06-11T18:03:26Z)
• FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis [55.939957482776194]
  We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
  arXiv  Detail & Related papers  (2022-01-22T02:50:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.