Ethical and Computational Challenges in Fairness-Aware Federated Learning in Healthcare

Abstract

Federated learning is a way to train machine learning models for healthcare institutions collaboratively without sharing sensitive patient data. It maintains privacy standards like HIPAA and GDPR while using diverse datasets to improve accuracy and inclusivity. However, integrating fairness in FL models, which includes ensuring algorithms do not discriminate based on race, gender, or socioeconomic status, is critical to prevent making bad healthcare disparities even worse, such as biased diagnosis in underrepresented groups. In this paper, we have analyzed the ethical and computational challenges that occur during the implementation of fairness-aware FL in healthcare. Ethical challenges include inequitable participation, privacy risks, bias amplification, accountability gaps, and cultural insensitivity, while computational challenges include non-IID data, high resource demands, fairness-accuracy trade-offs, scalability issues, and interpretability limitations. We have also included strategies to mitigate this, including fairness-aware aggregation, lightweight FL frameworks, and policy-algorithm co-design, to handle these challenges. In this paper, we offer a novel synthesis of ethical and technical perspectives, providing a roadmap for the development of fair and trustworthy FL systems by bringing together ideas from AI, ethical thinking, and healthcare guidelines. We have also mentioned future directions, such as standardized fairness metrics and federated explainable AI tools. It\'s important to solve these problems so that federated learning doesn\'t make health inequalities worse. Working together from different files is key to building fair and private healthcare AI systems that can make a big difference.

Introduction

1. Ethical Challenges

• Access and Equity
  Smaller hospitals (e.g., rural clinics) often lack the technical infrastructure to join FL networks, leading to exclusion of marginalized populations. This creates biased models that may underperform for underrepresented groups.
  Solution: Provide cloud subsidies, design lightweight FL tools for low-resource environments.

• Privacy and Accountability
  Even without raw data sharing, privacy risks persist through gradient leakage and inference attacks.
  Solution: Techniques like differential privacy and audit trails can mitigate risks and improve transparency.

• Cultural Sensitivity and Fairness
  FL systems can misinterpret data due to cultural differences in health expression, leading to biased predictions.
  Solution: Develop fairness metrics that account for cultural diversity and localized health behaviors.

2. Computational Challenges

• Non-IID Data and Bias
  Hospitals have different data distributions (urban vs rural), which makes training fair, accurate models difficult.
  Solution: Use approaches like agnostic FL, though they increase computational complexity.

• High Resource Requirements
  Techniques like adversarial fairness training increase GPU and memory usage significantly.
  Solution: Optimize trade-offs between fairness, scalability, and performance.

• Scalability and Communication Overhead
  As FL networks grow, coordination and bandwidth demands rise.
  Solution: Use gradient compression or clustered FL, though they may sacrifice fairness-relevant data.

• Explainability and Interpretability
  Trust in AI models depends on being able to explain predictions.
  Solution: Tools like federated SHAP help, but increase processing time by ~40% per round.

Literature Insights & Gaps

• FL has shown promise in disease prediction, sepsis detection, and cancer screening.

• Studies show models often perform worse for underserved populations due to data and resource inequality.

• Few solutions combine both ethical and computational approaches.

• There is no widely adopted standard for auditing, cultural fairness, or explainability in FL.

Contributions of the Paper

This study:

• Unifies ethical and technical perspectives on fairness in healthcare FL.

• Proposes practical strategies for inclusive participation, culturally adaptive fairness, and efficient computation.

• Serves as a policy-aligned guide for developing trustworthy, fair, and privacy-preserving AI systems in healthcare.

References

[1] B. McMahan et al., \"Communication-Efficient Learning of Deep Networks from Decentralized Data,\" AISTATS, 2017. [2] J. Xu et al., \"Federated Learning for Healthcare Informatics,\" J. Healthcare Informatics Res., vol. 5, pp. 1–19, 2021. [3] Z. Obermeyer et al., \"Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations,\" Science, vol. 366, no. 6464, pp. 447–453, 2019. [4] K. Holstein et al., \"Improving Accountability in Federated Learning,\" FAT/ML, 2022. [5] T. Li et al., \"Fair Resource Allocation in Federated Learning,\" ICLR, 2020. [6] L. Melis et al., \"Exploiting Unintended Feature Leakage in Collaborative Learning,\" IEEE S&P, pp. 691–706, 2019. [7] M. Abadi et al., \"Deep Learning with Differential Privacy,\" ACM CCS, pp. 308–318, 2016. [8] M. Mohri et al., \"Agnostic Federated Learning,\" Proc. ICML, pp. 4615–4625, 2019. [9] B. Zhang et al., \"Mitigating Unwanted Biases with Adversarial Learning,\" Proc. AIES, pp. 335–340, 2018. [10] J. Kone?ný et al., \"Federated Learning: Strategies for Improving Communication Efficiency,\" arXiv:1610.05492, 2016. [11] C. Briggs et al., \"Federated Learning with Hierarchical Clustering,\" FL-IJCAI, 2022. [12] H. Wang et al., \"FedSHAP: Federated Interpretable Machine Learning,\" Proc. NeurIPS, vol. 35, pp. 28171–28184, 2022. [13] M. Ribeiro et al., \"Why Should I Trust You? Explaining the Predictions of Any Classifier,\" Proc. SIGKDD, pp. 1135–1144, 2016. [14] J. Xu et al., \"Advancements in Federated Learning for Healthcare,\" J. Healthcare Informatics Res., vol. 7, pp. 45–67, 2023. [15] B. Zhang et al., \"FairFed: Fairness-Aware Federated Learning,\" Proc. AIES, pp. 350–356, 2020. [16] F. Sattler et al., \"Federated Coresets for Collaborative Machine Learning,\" IEEE IoT J., vol. 10, no. 1, pp. 417–429, 2023. [17] Y. Ruan et al., \"Optimizing Fairness in Federated Learning with Gradient Compression,\" Proc. ICML, pp. 7823–7835, 2023.

Copyright

Copyright © 2025 Shrinesh Rawool, Sanchita Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.