On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks

• URL: http://arxiv.org/abs/2404.18519v3
• Date: Thu, 5 Sep 2024 12:24:52 GMT
• Title: On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
• Authors: Usevalad Milasheuski, Luca Barbieri, Bernardo Camajori Tedeschini, Monica Nicoli, Stefano Savazzi,
• Abstract summary: Federated Learning (FL) allows privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the intricacies of medical data.
• Score: 3.9058850780464884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. However, the inherent challenge lies in the high heterogeneity of medical data, necessitating sophisticated techniques for assessment and compensation. This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the intricacies of medical data. In particular, we address the evaluation and comparison of the most popular FL algorithms with respect to their ability to cope with quantity-based, feature and label distribution-based heterogeneity. The goal is to provide a quantitative evaluation of the impact of data heterogeneity in FL systems for healthcare networks as well as a guideline on FL algorithm selection. Our research extends beyond existing studies by benchmarking seven of the most common FL algorithms against the unique challenges posed by medical data use cases. The paper targets the prediction of the risk of stroke recurrence through a set of tabular clinical reports collected by different federated hospital silos: data heterogeneity frequently encountered in this scenario and its impact on FL performance are discussed.

Related papers

• FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data [52.55123685248105]
  Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. This paper presents the first real-world FL benchmark for cardiovascular disease detection, named FedCVD.
  arXiv  Detail & Related papers  (2024-10-28T02:24:01Z)
• Federated Impression for Learning with Distributed Heterogeneous Data [19.50235109938016]
  Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data. In FL, sub-optimal convergence is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression.
  arXiv  Detail & Related papers  (2024-09-11T15:37:52Z)
• 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)
• Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities [9.476402318365446]
  In this work, we introduce a novel FL architecture designed to accommodate not only the heterogeneity of data samples, but also the inherent heterogeneity/non-uniformity of data modalities across institutions. We propose a solution by devising a distributed gradient blending and proximity-aware client weighting strategy tailored for multi-modal FL.
  arXiv  Detail & Related papers  (2024-01-07T23:45:01Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• 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)
• Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
  Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. We propose to model the behavior-related multi-source data streams with a local-global graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2021-03-23T11:10:04Z)
• A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources [5.049057348282933]
  Federated learning is an appealing framework for analyzing sensitive data from distributed health data networks. We develop an efficient and interpretable tree-based ensemble of personalized treatment effect estimators to join results across hospital sites.
  arXiv  Detail & Related papers  (2021-03-10T18:51:30Z)
• Inverse Distance Aggregation for Federated Learning with Non-IID Data [48.48922416867067]
  Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. We propose IDA, a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data.
  arXiv  Detail & Related papers  (2020-08-17T23:20:01Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

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.