FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems
Not on the map. Why?
Abstract (arXiv)
Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistical heterogeneity arising from differences in patient demographics, treatments, and outcomes, and infrastructure constraints. We introduce FedMAP, a personalised FL (PFL) framework that addresses heterogeneity through local Maximum a Posteriori (MAP) estimation with Input Convex Neural Network priors. These priors represent global knowledge gathered from other sites that guides the model while adapting to local data, and we provide a formal proof of convergence. Unlike many PFL methods that rely on fixed regularisation, FedMAP's prior adaptively learns patterns that capture complex inter-site relationships. We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets: 10-year cardiovascular risk prediction (CPRD, 387 general practitioner practices, 258,688 patients), iron deficiency detection (INTERVAL, 4 donor centres, 31,949 blood donors), and mortality prediction (eICU, 150 hospitals, 44,842 patients). FedMAP incorporates a three-tier design that enables participation across healthcare sites with varying infrastructure and technical capabilities, from full federated training to inference-only deployment. Geographical analysis reveals substantial equity improvements, with underperforming regions achieving up to 14.3% performance gains. This framework provides the first practical pathway for large-scale healthcare FL deployment, which ensures clinical sites at all scales can benefit, equity is enhanced, and privacy is retained.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 47 references that are in the map. First posted on arXiv 29 May 2024.
Builds on
19 of 47 references.
References · 19
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
- 2020The Future of Digital Health with Federated Learningcited by 633
- 2020Personalized Federated Learning with Moreau Envelopescited by 618
- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
- 2021Federated Learning Based on Dynamic Regularizationcited by 562
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
- 2016Input Convex Neural Networkscited by 414
- 2019Fair Resource Allocation in Federated Learningcited by 394
7 more
- 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
- 2021Personalized Federated Learning with Gaussian Processescited by 76
- 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
- 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
- 2023Bayesian Federated Learning: A Surveycited by 22
- 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
- 2023Privacy-preserving patient clustering for personalized federated learningcited by 4
Built on by
Citing papers per year: 2026: 1.
1 paper.