mlsanityData to 14 Oct 2026

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

arXiv 2601.04587 ↗First posted 8 Jan 2026cs.LG cs.AIcited by 0

On the map: Knowledge distillation → Federated, robust and private distillation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Federated, robust and private distillation, one of 12 directions in Knowledge distillation. Builds on 10 of its 21 references that are in the map. First posted on arXiv 8 Jan 2026.

Builds on

10 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillation30 Aug

Other references · 9

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2020The Future of Digital Health with Federated Learningcited by 633
  8. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  9. 2020Federated Transfer Learning: concept and applicationscited by 32

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
  2. 2025zkFL-Health: Blockchain-Enabled Zero-Knowledge Federated Learning for Medical AI Privacy
  3. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
  4. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification
  5. 2024FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
  6. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
  7. 2026FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
  8. 2024FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning