mlsanityData to 14 Oct 2026

Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis

arXiv 2412.19654 ↗First posted 27 Dec 2024cs.LG cs.DCcited by 2

Not on the map. Why?

Abstract (arXiv)

Geographic health disparities pose a pressing global challenge, particularly in underserved regions of low- and middle-income nations. Addressing this issue requires a collaborative approach to enhance healthcare quality, leveraging support from medically more developed areas. Federated learning emerges as a promising tool for this purpose. However, the scarcity of medical data and limited computation resources in underserved regions make collaborative training of powerful machine learning models challenging. Furthermore, there exists an asymmetrical reciprocity between underserved and developed regions. To overcome these challenges, we propose a novel cross-silo federated learning framework, named FedHelp, aimed at alleviating geographic health disparities and fortifying the diagnostic capabilities of underserved regions. Specifically, FedHelp leverages foundational model knowledge via one-time API access to guide the learning process of underserved small clients, addressing the challenge of insufficient data. Additionally, we introduce a novel asymmetric dual knowledge distillation module to manage the issue of asymmetric reciprocity, facilitating the exchange of necessary knowledge between developed large clients and underserved small clients. We validate the effectiveness and utility of FedHelp through extensive experiments on both medical image classification and segmentation tasks. The experimental results demonstrate significant performance improvement compared to state-of-the-art baselines, particularly benefiting clients in underserved regions.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 53 references that are in the map. First posted on arXiv 27 Dec 2024.

Builds on

23 of 53 references.

References · 23

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  8. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  9. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  10. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  11. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  12. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
11 more
  1. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
  2. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  3. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
  4. 2023FedGH: Heterogeneous Federated Learning with Generalized Global Headercited by 56
  5. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 52
  6. 2022No One Left Behind: Inclusive Federated Learning over Heterogeneous Devicescited by 37
  7. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  8. 2023Towards Personalized Federated Learning via Heterogeneous Model Reassemblycited by 31
  9. 2021Bidirectional Distillation for Top-K Recommender Systemcited by 21
  10. 2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learningcited by 11
  11. 2024FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Modelscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneitycited by 2
  2. 2026FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settingscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Federated Learning for Data and Model Heterogeneity in Medical Imaging
  2. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification
  3. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
  4. 2026FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems
  5. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmark
  6. 2025A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
  7. 2026Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts
  8. 2025Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift