Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data

• URL: http://arxiv.org/abs/2601.17183v1
• Date: Fri, 23 Jan 2026 21:18:08 GMT
• Title: Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data
• Authors: Farzam Asad, Junaid Saif Khan, Maria Tariq, Sundus Munir, Muhammad Adnan Khan,
• Abstract summary: This paper presents a comprehensive simulation research of Federated Proximal Optimization (FedProx) for Heart Disease prediction based on UCI Heart Disease dataset.<n>We generate realistic non-IID data partitions by simulating four heterogeneous hospital clients from the Cleveland Clinic.<n>Our results are directly transferable to hospital IT-administrators, implementing privacy-preserving collaborative learning.
• Score: 1.620240963217448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy regulations like HIPAA and GDPR prevent hospitals from directly sharing data with one another. Federated Learning offers a way out to this problem by facilitating collaborative model training without having the raw patient data centralized. However, clinical datasets intrinsically have non-IID (non-independent and identically distributed) features brought about by demographic disparity and diversity in disease prevalence and institutional practices. This paper presents a comprehensive simulation research of Federated Proximal Optimization (FedProx) for Heart Disease prediction based on UCI Heart Disease dataset. We generate realistic non-IID data partitions by simulating four heterogeneous hospital clients from the Cleveland Clinic dataset (303 patients), by inducing statistical heterogeneity by demographic-based stratification. Our experimental results show that FedProx with proximal parameter mu=0.05 achieves 85.00% accuracy, which is better than both centralized learning (83.33%) and isolated local models (78.45% average) without revealing patient privacy. Through generous sheer ablation studies with statistical validation on 50 independent runs we demonstrate that proximal regularization is effective in curbing client drift in heterogeneous environments. This proof-of-concept research offers algorithmic insights and practical deployment guidelines for real-world federated healthcare systems, and thus, our results are directly transferable to hospital IT-administrators, implementing privacy-preserving collaborative learning.

Related papers

• Federated Learning for Pediatric Pneumonia Detection: Enabling Collaborative Diagnosis Without Sharing Patient Data [0.20878272814614088]
  Early and accurate pneumonia detection from chest X-rays is clinically critical to expedite treatment and isolation, reduce complications, and curb unnecessary antibiotic use.<n>Development of CXR-based detection is hindered by globally distributed data, high inter-hospital variability, and strict privacy regulations.<n>In this paper, we evaluate Federated Learning (FL) using the Sherpa.ai FL platform.<n>FL delivers high-performing, generalizable, secure and private pneumonia detection across healthcare networks.
  arXiv  Detail & Related papers  (2025-11-12T18:17:06Z)
• 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)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models [8.798959872821962]
  This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications.
  arXiv  Detail & Related papers  (2024-07-20T18:34:20Z)
• Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures [0.8132630541462695]
  In this study, we leverage federated learning strategies to address clopidogrel treatment failure detection. We partitioned the data based on geographic centers and evaluated the performance of federated learning. Our findings underscore the potential of federated learning in addressing clopidogrel treatment failure detection.
  arXiv  Detail & Related papers  (2024-03-05T23:31:07Z)
• Differentially Private Distributed Inference [2.4401219403555814]
  Healthcare centers collaborating on clinical trials must balance knowledge sharing with safeguarding sensitive patient data.<n>We address this challenge by using differential privacy (DP) to control information leakage.<n>Agents update belief statistics via log-linear rules, and DP noise provides plausible deniability and rigorous performance guarantees.
  arXiv  Detail & Related papers  (2024-02-13T01:38:01Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.