mlsanityData to 7 Oct 2026

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI

arXiv 2510.06259 ↗First posted 5 Oct 2025cs.CY cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on heterogeneity and task complexity, (2) Fairness-Aware Distillation using influence-weighted aggregation, and (3) Curriculum-Guided Acceleration reducing rounds by 60-70%. Our theoretical analysis provides convergence guarantees with epsilon-fairness bounds, achieving O(T^{-1/2}) + O(H_max/T^{3/4}) rates. Projected results show 55-75% communication reduction, 56-68% fairness improvement, 34-46% energy savings, and 100+ institution support. The framework enables multi-modal integration across imaging, genomics, EHR, and sensor data while maintaining HIPAA/GDPR compliance. We propose MedFedBench benchmark suite for standardized evaluation across six healthcare dimensions: convergence efficiency, institutional fairness, privacy preservation, multi-modal integration, scalability, and clinical deployment readiness. Economic projections indicate 400-800% ROI for rural hospitals and 15-25% performance gains for academic centers. This work presents a seven-question research agenda, 24-month implementation roadmap, and pathways toward democratizing healthcare AI.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 5 Oct 2025.

Builds on

30 of 49 references.

References · 30

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2016Deep Learning with Differential Privacycited by 2,682
  7. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  12. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
18 more
  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  2. 2020The Future of Digital Health with Federated Learningcited by 625
  3. 2019Green AIcited by 620
  4. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  5. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  8. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  9. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  10. 2019Agnostic Federated Learningcited by 451
  11. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  12. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  13. 2021A Field Guide to Federated Optimizationcited by 312
  14. 2019Asynchronous Federated Optimizationcited by 264
  15. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  16. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  17. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  18. 2022Personalized Federated Learning via Heterogeneous Modular Networkscited by 8

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Feature Selection and Regularization in Multi-Class Classification: An Empirical Study of One-vs-Rest Logistic Regression with Gradient Descent Optimization and L1 Sparsity Constraintscited by 0
  2. 2025AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chainingcited by 0