Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912665559891968 |
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| author | Arafat, Jahidul Tasmin, Fariha Poudel, Sanjaya Haider, Iftekhar |
| author_facet | Arafat, Jahidul Tasmin, Fariha Poudel, Sanjaya Haider, Iftekhar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06259 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI Arafat, Jahidul Tasmin, Fariha Poudel, Sanjaya Haider, Iftekhar Computers and Society Machine Learning 68T05, 62P10, 68T07 I.2.11; K.4.1; J.3 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. |
| title | Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI |
| topic | Computers and Society Machine Learning 68T05, 62P10, 68T07 I.2.11; K.4.1; J.3 |
| url | https://arxiv.org/abs/2510.06259 |