mlsanityData to 7 Oct 2026

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

arXiv 2404.18519 ↗First posted 29 Apr 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

14 of 25 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  6. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  8. 2019Federated Machine Learning: Concept and Applicationscited by 409
  9. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  10. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  11. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  12. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
2 more
  1. 2021Identifying Stroke Indicators Using Rough Setscited by 4
  2. 2022Fed-TDA: Federated Tabular Data Augmentation on Non-IID Datacited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Bayesian Federated Learning for Continual Trainingcited by 0
  2. 2025BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneitycited by 0
  3. 2025CoGraM: Context-sensitive granular optimization method with rollback for robust model fusioncited by 0