mlsanityData to 7 Oct 2026

Fair Federated Medical Image Segmentation via Client Contribution Estimation

arXiv 2303.16520 ↗First posted 29 Mar 2023cs.LG cs.AI cs.CVcited by 50

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Abstract (arXiv)

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 35 of its 60 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

35 of 60 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  7. 2020The Future of Digital Health with Federated Learningcited by 625
  8. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  10. 2019Agnostic Federated Learningcited by 451
  11. 2019Fair Resource Allocation in Federated Learningcited by 389
  12. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
23 more
  1. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  2. 2020Fairness in Machine Learningcited by 194
  3. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
  4. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  5. 2020Threats to Federated Learning: A Surveycited by 171
  6. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  7. 2020MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Datacited by 121
  8. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  9. 2021Distributionally Robust Federated Averagingcited by 104
  10. 2020Collaborative Fairness in Federated Learningcited by 95
  11. 2020Fairness-aware Agnostic Federated Learningcited by 87
  12. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  13. 2020A Distributional Framework for Data Valuationcited by 79
  14. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  15. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
  16. 2020Federated Learning Meets Multi-objective Optimizationcited by 50
  17. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  18. 2021Improving Fairness via Federated Learningcited by 39
  19. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  20. 2020Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Datasetcited by 34
  21. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  22. 2020Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Datacited by 15
  23. 2022Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensemblingcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 20; 2025: 16; 2026: 9.

50 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
  3. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 26
  4. 2024Redefining Contributions: Shapley-Driven Federated Learningcited by 16
  5. 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
  6. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  7. 2024Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentationcited by 6
  8. 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
  9. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  10. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  11. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  12. 2024Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRIcited by 5
38 more
  1. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  2. 2023Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classificationcited by 4
  3. 2024Empowering Federated Learning for Massive Models with NVIDIA FLAREcited by 4
  4. 2024Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosiscited by 4
  5. 2025CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learningcited by 4
  6. 2023Exploring Data Redundancy in Real-world Image Classification through Data Selectioncited by 3
  7. 2024FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completenesscited by 2
  8. 2024FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learningcited by 2
  9. 2024A New Perspective to Boost Performance Fairness for Medical Federated Learningcited by 2
  10. 2024IPMN Risk Assessment under Federated Learning Paradigmcited by 2
  11. 2024Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosiscited by 2
  12. 2025FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentationcited by 2
  13. 2025FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysiscited by 2
  14. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  15. 2025Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentationcited by 2
  16. 2026Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneitycited by 2
  17. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  18. 2024Seamless Integration: Sampling Strategies in Federated Learning Systemscited by 1
  19. 2024Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentivescited by 1
  20. 2025Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentationcited by 1
  21. 2025FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRAcited by 1
  22. 2025Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classificationcited by 1
  23. 2026OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imagingcited by 1
  24. 2026SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Rankscited by 1
  25. 2024Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregationcited by 0
  26. 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environmentscited by 0
  27. 2025Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matchingcited by 0
  28. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  29. 2025How to Evaluate Participant Contributions in Decentralized Federated Learningcited by 0
  30. 2025A case for data valuation transparency via DValCardscited by 0
  31. 2025FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimationcited by 0
  32. 2025LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mappingcited by 0
  33. 2026FeTTL: Federated Template and Task Learning for Multi-Institutional Medical Imagingcited by 0
  34. 2026CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillationcited by 0
  35. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
  36. 2026FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocationcited by 0
  37. 2026Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropycited by 0
  38. 2026Class-wise Contribution Estimation via Logit Maximization for Federated Learningcited by 0