Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

A Decentralized Federated Learning Framework for Privacy-Preserving Malnutrition Risk Prediction in Children

Domain:

healthcare

Record type:

paper
Creator:
ShrM. Gop
Publisher:
EDP
Host:
Malnutrition among children under five remains a critical public health challenge in low-income countries, including Ethiopia. The implementation of centralised machine learning methods faces difficulties because they violate patient privacy laws and existing health system limitations in resource-scarce environments. This paper presents a decentralised federated learning (DFL) framework using the ProxyFL gossip architecture, enabling collaborative malnutrition risk prediction across distributed nodes without any raw data sharing. Each node independently trains a Random Forest (RF) or XGBoost (XGB) classifier on a local partition of an Ethiopian pediatric dataset, exchanging only Fernet-encrypted feature importance vectors as proxy representations. The research examines the three-client federated system, which distributes its dataset among three separate health nodes that each contain approximately 534 training samples after removing the outliers. The study evaluates RF and XGBoost performance across all three client partitions by conducting a direct side-by-side assessment. The three clients show RF test AUC-ROC results of 0.721, 0.718 and 0.656, while XGBoost delivers results of 0.689, 0.674 and 0.539. The ProxyFL gossip protocol is validated as stable and algorithm-agnostic across all 20 communication rounds. SHAP analysis confirms BMI as the universally dominant predictor, with clinically coherent secondary features across all client partitions.

Visit

doi.org

Languages

Amharic

Licenses

https://creativecommons.org/licenses/by/4.0/

Similar

Privacy-Preserving Federated Learning Framework for Sustainable Crop Yield Prediction Across Distributed Smallholder Farm NetworksPrivacy-Preserving Federated Learning for Hate Speech DetectionMulti-scenario evaluation of federated learning for privacy-preserving malaria prediction with Ghana DHS dataFederated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacyFederated Learning Enables Privacy-Preserving Cross-Border Malaria SurveillanceDesigning a Privacy-Preserving Behavioral Phishing Detection Artifact: A Design Science Framework Using Federated Differential Privacy

Privacy-Preserving Federated Learning Framework for Sustainable Crop Yield Prediction Across Distributed Smallholder Farm Networks

An accurate forecast of the crop yield is crucial for food security but smallholder farmers in devel

Privacy-Preserving Federated Learning for Hate Speech Detection

This paper presents a federated learning system with differential privacy for hate speech detection,

Multi-scenario evaluation of federated learning for privacy-preserving malaria prediction with Ghana DHS data

Improving malaria prediction in Ghana requires data from across its health system, yet Ghana’s Data

Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy

The high demand of artificial intelligence services at the edges that also preserve data privacy has

Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance

Malaria surveillance in West Africa faces two conflicting imperatives:

    Designing a Privacy-Preserving Behavioral Phishing Detection Artifact: A Design Science Framework Using Federated Differential Privacy

    Current detection frameworks largely analyze emails as technical artifacts while overlooking the beh